Migrasomes are a newly discovered type of extracellular vesicle (EV) formed during cell migration, playing a pivotal role in intercellular communication. These vesicles are generated by retracting fibers of migrating cells and encapsulate various molecules, such as proteins, lipids, and RNA, allowing the transfer of biochemical signals to neighboring cells. Current evidence suggests that migrasomes are involved in a wide range of physiological processes such as embryogenesis, angiogenesis, immune modulation, and mitochondrial quality control. Moreover, migrasomes are implicated in pathological conditions, including cancer metastasis, cardiovascular diseases, and viral infections. To fully understand their significance, it is critical to first explore the molecular mechanisms underlying their formation and function. Recent studies have shed light on the biogenesis, release, and biological properties of migrasomes, all of which are key to understanding their role in cell-to-cell communication. In this review, we provide an up-to-date summary of migrasome biogenesis, release, characterization, and their biological activities in intercellular communication, while also proposing potential new functions for these vesicles.

The metabolism of arginine, a conditionally essential amino acid, plays a crucial role in cancer progression and prognosis. However, a more detailed understanding of the influence of arginine biosynthesis genes in cancer is currently unavailable. We performed an integrative multi-omics analysis using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to determine the characteristics of these genes across multiple cancer types. To measure the overall activity of arginine biosynthesis genes in cancer, we calculated arginine biosynthesis scores based on gene expression. Our results indicated that the arginine biosynthesis score was negatively correlated with immune-related pathways, immune infiltration, immune checkpoint expression, and patient prognosis, and single-cell data further clarified that patients with high arginine biosynthesis scores showed a reduced proportion of T and B cells in an immune desert tumor microenvironment and were insensitive to immunotherapy. We also identified several potential drugs through the Cancer Therapeutic Response Portal (CTRP) and Genomics of Drug Sensitivity in Cancer (GDSC) databases that could target arginine biosynthesis genes and potentially improve the response rate to immunotherapy in patients with a high arginine biosynthesis fraction. Overall, our analyses emphasize that arginine biosynthesis genes are associated with immune evasion in several cancers. Targeting these genes may facilitate more effective immunotherapy.

Background: Patients with bladder cancer (BLCA) have a poor prognosis and little progress has been made in treatment. Therefore, the purpose of this work was to employ Mendelian randomization (MR) and transcriptome analysis to identify a novel biomarker that could be used to reliably diagnose BLCA.

Background: As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA. Methods: Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package “Seurat” to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package “ConsensusClusterPlus” in the bulk RNA-seq data. Signature genes were identified by the R package “limma” and univariate regression analysis to calculate risk scores using the R package “GSVA” and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated. Results: 5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response. Conclusion: We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.

Bladder cancer (BLCA) is a prevalent malignancy with substantial consequences for patient health. This study aimed to elucidate the underlying mechanisms of BLCA through integrated multi-omics analysis. Tumor and adjacent tissues from BLCA patients underwent transcriptomic, whole-exome sequencing, metabolomic, and intratumoral microbiome analyses. These data were integrated with public datasets to identify key genes, metabolites, and microorganisms. Molecular subtypes were defined by key gene expression and compared for pathways, immune profiles, mutations, immunotherapy response, and drug sensitivity. Prognostic relevance was validated in external cohorts. Single-cell sequencing was applied to reveal cellular localization of key genes. Three key genes (AHNAK, CSPG4, NCAM1), 90 metabolites, and two microbes (Sphingomonas koreensis, Rhodospirillaceae) were identified. Key genes negatively correlated with metabolites but not with microbes. BLCA samples were classified into two molecular clusters with distinct ECM organization, metabolic features, immune checkpoint expression, and therapeutic sensitivity. NCAM1 correlated positively with γδ T cells and negatively with M0 macrophages. Single-cell analysis revealed nine major cell types, with fibroblasts displaying the highest expression of key genes, particularly elevated AHNAK in specific fibroblast subtypes. Drug prediction and docking identified candidate compounds targeting these genes with stable binding potential. This comprehensive multi-omics analysis links key genes, metabolites, and microbes to BLCA pathogenesis. Fibroblasts emerge as central regulators, while identified gene-metabolite interactions and microbial associations provide novel insights into tumor heterogeneity. These findings highlight potential biomarkers and therapeutic targets to support precision treatment in BLCA.

Introduction: Bladder cancer (BCa) is one of the top ten most common cancers, yet its underlying mechanisms remain unclear. This study aimed to explore the potential molecular mechanisms of BCa using multi-omics and single-cell analysis. Methods: First, differential analysis of transcriptome data related to BCa from public databases was performed, and a risk model was then developed using 101 different machine learning algorithms to determine prognostic genes, followed by independent prognostic analysis to construct a nomogram. Immune infiltration analysis was performed to explore the impact of prognostic genes on the tumor microenvironment. Metabolomics, proteomics, and post-translational modification data from BCa tumor and adjacent non-tumor tissues were used to explore the relationships between prognostic genes and various omics levels. Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes. Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. Furthermore, risk scores, pathological T stage, and pathological N stage were confirmed as independent prognostic factors for the nomogram construction. Interestingly, high-risk patients showed a significantly lower response to PD-L1 treatment, with higher TIDE scores. Omics analysis revealed a close relationship between prognostic genes and proteomics, metabolomics, and post-translational modifications. Specifically, FLNC and MYH11 may influence BCa progression through phosphorylation and succinylation. Single-cell analysis identified fibroblasts as key cells in BCa. Functional experiments showed that MYH11 knockdown promoted cell proliferation, migration, and invasion. Conclusion: This study identified 8 prognostic genes to construct a risk model, and suggest that MYH11 is a potential diagnostic and prognostic biomarker for BCa.