Explore the vast range of titles available.

Although immunotherapy has made breakthrough progress, its efficacy in solid tumours remains unsatisfactory. Exosomes are the main type of extracellular vesicles that can deliver various intracellular molecules to adjacent or distant cells and organs, mediating various biological functions. Studies have found that exosomes can both activate the immune system and inhibit the immune system. The antigen and major histocompatibility complex (MHC) carried in exosomes make it possible to develop them as anticancer vaccines. Exosomes derived from blood, urine, saliva and cerebrospinal fluid can be used as ideal biomarkers in cancer diagnosis and prognosis. In recent years, exosome-based therapy has made great progress in the fields of drug transportation and immunotherapy. Here, we review the composition and sources of exosomes in the solid cancer immune microenvironment and further elaborate on the potential mechanisms and pathways by which exosomes influence immunotherapy for solid cancers. Moreover, we summarize the potential clinical application prospects of engineered exosomes and exosome vaccines in immunotherapy for solid cancers. Eventually, these findings may open up avenues for determining the potential of exosomes for diagnosis, treatment, and prognosis in solid cancer immunotherapy.

Cancer immunotherapy has transformed traditional treatments, with immune checkpoint blockade being particularly prominent. However, immunotherapy has minimal benefit for patients in most types of cancer and is largely ineffective in some cancers (such as pancreatic cancer and glioma). A synergistic anti-tumor response may be produced through the combined application with traditional tumor treatment methods. Radiotherapy (RT) not only kills tumor cells but also triggers the pro-inflammatory molecules’ release and immune cell infiltration, which remodel the tumor microenvironment (TME). Therefore, the combination of RT and immunotherapy is expected to achieve improved efficacy. In this review, we summarize the effects of RT on cellular components of the TME, including T cell receptor repertoires, different T cell subsets, metabolism, tumor-associated macrophages and other myeloid cells (dendritic cells, myeloid-derived suppressor cells, neutrophils and eosinophils). Meanwhile, non-cellular components such as lactate and extracellular vesicles are also elaborated. In addition, we discuss the impact of different RT modalities on tumor immunity and issues related to the clinical practice of combination therapy.

Tumor immunotherapy has transformed neoplastic disease management, yet low response rates and immune complications persist as major challenges. Extracellular vesicles including exosomes have emerged as therapeutic agents actively involved in a diverse range of pathological conditions. Mounting evidence suggests that alterations in the quantity and composition of extracellular vesicles (EVs) contribute to the remodeling of the immune-suppressive tumor microenvironment (TME), thereby influencing the efficacy of immunotherapy. This revelation has sparked clinical interest in utilizing EVs for immune sensitization. In this perspective article, we present a comprehensive overview of the origins, generation, and interplay among various components of EVs within the TME. Furthermore, we discuss the pivotal role of EVs in reshaping the TME during tumorigenesis and their specific cargo, such as PD-1 and non-coding RNA, which influence the phenotypes of critical immune cells within the TME. Additionally, we summarize the applications of EVs in different anti-tumor therapies, the latest advancements in engineering EVs for cancer immunotherapy, and the challenges encountered in clinical translation. In light of these findings, we advocate for a broader understanding of the impact of EVs on the TME, as this will unveil overlooked therapeutic vulnerabilities and potentially enhance the efficacy of existing cancer immunotherapies.

Tumor angiogenesis plays vital roles in the growth and metastasis of cancer. RNA methylation is one of the most common modifications and is widely observed in eukaryotes and prokaryotes. Accumulating studies have revealed that RNA methylation affects the occurrence and development of various tumors. In recent years, RNA methylation has been shown to play an important role in regulating tumor angiogenesis. In this review, we mainly elucidate the mechanisms and functions of RNA methylation on angiogenesis and progression in several cancers. We then shed light on the role of RNA methylation-associated factors and pathways in tumor angiogenesis. Finally, we describe the role of RNA methylation as potential biomarker and novel therapeutic target.

Entity resolution technology is the process of distinguishing whether data from different knowledge bases refer to the same entity in the real world. Existing research takes entity pairs as input and makes judgments based on the characteristics of entity pairs. However, there is insufficient utilization of contextual semantics, as existing methods fail to effectively model the token-attribute associations within data sources and cross-attribute semantic hierarchical relationships, which weakens the discriminative power of key attributes. What’ more, they exhibit failure in handling polysemous ambiguities, as conventional graph neural network adopts rigid node representations that cannot dynamically adjust word meanings according to attribute-specific contexts. To address this issue, this paper proposes the Contextual Semantics Graph Attention Network (CSGAT), which extracts contextual information at token and attribute levels to generate semantically fused embeddings. The advantages of CSGAT are: 1) Leveraging the Transformer self-attention mechanism to extract feature vectors of words, model sequence relationships, and calculate the degree of relevance with other words; 2) Employing the attention mechanism on contextual information at the attribute level to extract semantic embeddings to enrich attribute embeddings, forming more discriminative attribute embeddings; 3) Utilizing the graph attention network to generate residual vectors for final entity resolution decisions. Experimental on Amazon-Google and BeerAdvo-RateBeer datasets show that, as compared with the competing methods, CSGAT can achieve significant improved performance on F1-score with fine Precision and Recall. Code is available at https://github.com/xhtech2024/csgat .

Federated learning is widely used for collaborative training of human activity recognition models across multiple devices with limited local data. However, label noise caused by human and time constraints during data annotation is common and severely limits model performance. Existing studies mainly address this through client selection and sample filtering, but still face key limitations: (1) insufficient granularity in client quality evaluation; (2) aggregation methods ignoring data quality differences; (3) client drift under non-IID data distribution. To overcome these challenges of complex label noise and feature drift, this paper proposes LN-FHAR, a two-stage federated learning framework with label noise robustness. This framework effectively mitigates the coupling problem of noise and data heterogeneity by assessing client data quality and designing differentiated training strategies. In the client selection stage, clients are graded based on class-level loss analysis and a Gaussian Mixture Model. In the noise-robust training stage, reliable neighbors are introduced to collaboratively filter clean samples, and prototype regularization is employed to constrain the consistency between local models and global feature representations. Additionally, a data-aware aggregation method is designed, which assigns weights based on both the quality and quantity of client data, reducing the negative impact of noisy clients. Experimental results demonstrate that LN-FHAR has robustness and generalization ability in complex noise environments.

Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging. This meta-analysis evaluates the effectiveness of AI in enhancing CT image quality and lowering radiation doses. A thorough literature search was performed across several databases, including PubMed, Embase, Web of Science, Science Direct, and Cochrane Library, with the final update in 2024. We included studies that compared AI-based interventions to conventional CT techniques. The quality of these studies was assessed using the Newcastle-Ottawa Scale. Random effect models were used to pool results, and heterogeneity was measured using the I² statistic. Primary outcomes included image quality, CT dose index, and diagnostic accuracy. This meta-analysis incorporated 5 clinical validation studies published between 2022 and 2024, totaling 929 participants. Results indicated that AI-based interventions significantly improved image quality (mean difference 0.70, 95% CI 0.43-0.96; P<.001) and showed a positive trend in reducing the CT dose index, though not statistically significant (mean difference 0.47, 95% CI -0.21 to 1.15; P=.18). AI also enhanced image analysis efficiency (odds ratio 1.57, 95% CI 1.08-2.27; P=.02) and demonstrated high accuracy and sensitivity in detecting intracranial aneurysms, with low-dose CT using AI reconstruction showing noninferiority for liver lesion detection. The findings suggest that AI-based interventions can significantly enhance CT imaging practices by improving image quality and potentially reducing radiation doses, which may lead to better diagnostic accuracy and patient safety. However, these results should be interpreted with caution due to the limited number of studies and the variability in AI algorithms. Further research is needed to clarify AI's impact on radiation reduction and to establish clinical standards.

To elucidate the historical trends, underlying causes and future projections of esophageal cancer incidence in Hong Kong. Utilizing the Age-Period-Cohort (APC) model, we analyzed data from the Hong Kong Cancer Registry (1992-2021) and United Nations World Population Prospects 2022 Revision. Age-standardized incidence rates were computed, and APC models evaluated age, period, and cohort effects. Bayesian APC modeling, coupled with decomposition analysis, projected future trends and identified factors influencing incidence. Between 1992 and 2021, both crude and age-standardized incidence rates of esophageal cancer witnessed significant declines. Net drifts exhibited pronounced downward trends for both sexes, with local drift diminishing across all age groups. Period and cohort rate ratios displayed a consistent monotonic decline for both sexes. Projections indicate a continued decline in esophageal cancer incidence. Population decomposition analysis revealed that epidemiological changes offset the increase in esophageal cancer cases due to population growth and aging. The declining trend of esophageal cancer in Hong Kong is influenced by a combination of age, period, and cohort. Sustaining and enhancing these positive trends requires continuous efforts in public health interventions.

Background Colon adenocarcinoma (COAD) is the most common type of colon cancer, posing a significant threat to public health. In the tumor microenvironment (TME), T cells differentiate into terminally exhausted T cells (TEX), but the relationship between TEX and COAD has not been fully elucidated. Methods To identify TEX-related signatures, we integrated transcriptomic data from TCGA and GEO databases (GSE103479, GSE17536). A prognostic model was constructed using GSVA, univariate Cox, LASSO, and random forest algorithms. The tumor immune microenvironment was characterized using CIBERSORTx and GSEA. The functional role of FAT4 was validated in vitro using FAT4-knockdown COAD cell lines assessed by flow cytometry and RT-qPCR. Results We developed a prognostic signature based on five TEX-related genes (IL21R, FCRL3, TIFAB, TNFSF14, SLAMF1). Patients in the high-risk group showed significantly poorer overall survival and distinct immune cell infiltration patterns, characterized by decreased CD8 + T cells and M1 macrophages. At single-cell resolution, CD8 + TEX cells exhibited high expression of immune checkpoints like LAG3. Furthermore, in vitro experiments demonstrated that knockdown of FAT4, a frequently mutated gene in COAD, promoted apoptosis and induced G0/G1 cell cycle arrest in COAD cells. Conclusion We proposed a non-invasive prediction method based on TEX-related genes, which effectively predicts survival outcomes and therapeutic responses in COAD patients. Additionally, FAT4 was found to regulate proliferative and apoptotic phenotypes in COAD.

Purpose Thoracic SMARCA4-deficient undifferentiated tumor (SD-UT) is a highly aggressive disease that is nosologically related to but distinct from SMARCA4-deficient non-small cell lung cancer (SD-NSCLC). No standard treatment guidelines were established for SD-UT. This research explored the efficacy of different treatments in SD-UT, and the prognostic, clinicopathologic and genomic difference between SD-UT and SD-NSCLC. Materials and methods Information of 25 SD-UT and 22 SD-NSCLC patients diagnosed and treated in Fudan University Shanghai Cancer Center from January, 2017 to September, 2022 was analyzed. Results SD-UT was similar to SD-NSCLC in characteristics of onset age, male prevalence, heavy smoking history and metastatic pattern. SD-UT showed a rapid relapse pattern after radical therapy. For Stage IV SD-UT patients, immune checkpoint inhibitor (ICI) plus chemotherapy significantly improved median progression-free survival (PFS) compared to traditional chemotherapy as first-line treatment (26.8 vs. 2.73 months, p  = 0.0437), while objective response rates of two arms were comparable (71.4% vs. 66.7%). No significant survival differences were observed between SD-UT and SD-NSCLC under similar treatment settings. SD-UT or SD-NSCLC patients receiving ICI in the first line had significantly prolonged OS than those with ICI in the latter lines or without ICI treatment throughout clinical courses. Genetic study found frequent SMARCA4, TP53 and LRP1B mutations in SD-UT. Conclusion To the best of our knowledge, this is the largest series to date to compare the efficacy of ICI-based treatment to chemotherapy and document frequent mutations of LRP1B in SD-UT. ICI plus chemotherapy is an effective strategy for Stage IV SD-UT.