Intelligent drug delivery is a promising strategy for cancer therapies. In recent years, with the rapid development of synthetic biology, some properties of bacteria, such as gene operability, excellent tumor colonization ability, and host-independent structure, make them ideal intelligent drug carriers and have attracted extensive attention. By implanting condition-responsive elements or gene circuits into bacteria, they can synthesize or release drugs by sensing stimuli. Therefore, compared with traditional drug delivery, the usage of bacteria for drug loading has better targeting ability and controllability, and can cope with the complex delivery environment of the body to achieve the intelligent delivery of drugs. This review mainly introduces the development of bacterial-based drug delivery carriers, including mechanisms of bacterial targeting to tumor colonization, gene deletions or mutations, environment-responsive elements, and gene circuits. Meanwhile, we summarize the challenges and prospects faced by bacteria in clinical research, and hope to provide ideas for clinical translation.

Natural extracellular vesicles (EVs) play important roles in many life processes such as in the intermolecular transfer of substances and genetic information exchanges. Investigating the origins and working mechanisms of natural EVs may provide an understanding of life activities, especially regarding the occurrence and development of diseases. Additionally, due to their vesicular structure, EVs (in small molecules, nucleic acids, proteins, etc.) could act as efficient drug-delivery carriers. Herein, we describe the sources and biological functions of various EVs, summarize the roles of EVs in disease diagnosis and treatment, and review the application of EVs as drug-delivery carriers. We also assess the challenges and perspectives of EVs in biomedical applications.

BACKGROUND: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. METHODS: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). RESULTS: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. CONCLUSIONS A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.
Humans ; Female ; Breast Neoplasms/genetics* ; East Asian People ; Neoplasm Recurrence, Local/genetics* ; Breast ; Algorithms ; Chronic Disease ; Prognosis ; Tumor Microenvironment