A tumor targeted nano micelle carrying astragaloside IV for combination treatment of bladder cancer.

Immune checkpoint inhibitors (ICIs) are effective agents for tumor immunotherapy. However, their clinical effectiveness is unsatisfactory due to off-target effects and a suppressive immune microenvironment. This study developed a nanodrug delivery system for bladder cancer (BCa) using PCL-MPEG and PCL-PEG-CHO to synthesize internal hydrophobic and external hydrophilic micelles (PP) that encapsulated water-insoluble astragaloside IV (PPA). The aldehyde group on the surface of PPA reacted with the amino group of aPD-L1, allowing the decoration of this antibody on the surface of the micelles. The resultingPPA@aPD-L1effectively piggybacked astragaloside IV and aPD-L1 antibody. These findings suggest that PPA@aPD-L1 is relatively stable in circulation and efficiently binds to BCa cells with the aid of aPD-L1. Additionally, this strategy prolongs the drug's retention time in tumors. Compared to PBS, PP, and PPA with PPA + aPD-L1 groups, PPA@aPD-L1significantly prolonged the survival of mice with BCa and reduced tumor volume. Mechanistic studies showed that PPA inhibited the NF-κB and STAT3 signaling pathways in tumor cells. Additionally, PPA@aPD-L1increased IFN-γ and decreased IL-10 expression in bladder tumors, affecting the number and type of intratumorally infiltrating T cells. Our study presents a simple and effective drug delivery system that combines herbal monomers with ICIs. It has demonstrated a potent ability to suppress tumor growth and holds potential for future applications.

Integrated analysis of single-cell and bulk RNA-sequencing reveals a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in gastric cancer.

Natural killer (NK) cells play essential roles in the tumor development, diagnosis, and prognosis of tumors. In this study, we aimed to establish a reliable signature based on marker genes in NK cells, thus providing a new perspective for assessing immunotherapy and the prognosis of patients with gastric cancer (GC). We analyzed a total of 1560 samples retrieved from the public database. We performed a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of gastric cancer and identified 377 marker genes for NK cells. By performing Cox regression analysis, we established a 12-gene NK cell-associated signature (NKCAS) for the Cancer Genome Atlas (TCGA) cohort, that assigned GC patients into a low-risk group (LRG) or a high-risk group (HRG). In the TCGA cohort, the areas under curve (AUC) value were 0.73, 0.81, and 0.80 at 1, 3, and 5 years. External validation of the predictive ability for the signature was then validated in the Gene Expression Omnibus (GEO) cohorts (GSE84437). The expression levels of signature genes were measured and validated in GC cell lines by real-time PCR. Moreover, NKCAS was identified as an independent prognostic factor by multivariate analysis. We combined this with a variety of clinicopathological characteristics (age, M stage, and tumor grade) to construct a nomogram to predict the survival outcomes of patients. Moreover, the LRG showed higher immune cell infiltration, especially CD8+ T cells and NK cells. The risk score was negatively associated with inflammatory activities. Importantly, analysis of the independent immunotherapy cohort showed that the LRG had a better prognosis and immunotherapy response when compared with the HRG. The identification of NK cell marker genes in this study suggests potential therapeutic targets. Additionally, the developed predictive signatures and nomograms may aid in the clinical management of GC.

Transcriptome sequencing reveals novel biomarkers and immune cell infiltration in esophageal tumorigenesis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies worldwide, and its development comprises a multistep process from intraepithelial neoplasia (IN) to carcinoma (CA). However, the critical regulators and underlying molecular mechanisms remain largely unknown. AIM: To explore the genes and infiltrating immune cells in the microenvironment that are associated with the multistage progression of ESCC to facilitate diagnosis and early intervention. METHODS: A mouse model mimicking the multistage development of ESCC was established by providing warter containing 4-nitroquinoline 1-oxide (4NQO) to C57BL/6 mice. Moreover, we established a control group without 4NQO treatment of mice. Then, transcriptome sequencing was performed for esophageal tissues from patients with different pathological statuses, including low-grade IN (LGIN), high-grade IN (HGIN), and CA, and controlled normal tissue (NOR) samples. Differentially expressed genes (DEGs) were identified in the LGIN, HGIN, and CA groups, and the biological functions of the DEGs were analyzed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The CIBERSORT algorithm was used to detect the pattern of immune cell infiltration. Immunohistochemistry (IHC) was also conducted to validate our results. Finally, the Luminex multiplex cytokine analysis was utilized to measure the serum cytokine levels in the mice. RESULTS: Compared with those in the NOR group, a total of 681541, and 840 DEGs were obtained in the LGIN, HGIN, and CA groups, respectively. Using the intersection of the three sets of DEGs, we identified 86 genes as key genes involved in the development of ESCC. Enrichment analysis revealed that these genes were enriched mainly in the keratinization, epidermal cell differentiation, and interleukin (IL)-17 signaling pathways. CIBERSORT analysis revealed that, compared with those in the NOR group, M0 and M1 macrophages in the 4NQO group showed stronger infiltration, which was validated by IHC. Serum cytokine analysis revealed that, compared with those in the NOR group, IL-1ß and IL-6 were upregulated, while IL-10 was downregulated in the LGIN, HGIN, and CA groups. Moreover, the expression of the representative key genes, such as S100a8 and Krt6b, was verified in external human samples, and the results of immunohistochemical staining were consistent with the findings in mice. CONCLUSION: We identified a set of key genes represented by S100a8 and Krt6b and investigated their potential biological functions. In addition, we found that macrophage infiltration and abnormal alterations in the levels of inflammation-associated cytokines, such as IL-1ß, IL-6, and IL-10, in the peripheral blood may be closely associated with the development of ESCC.

Predictive value of TCM tongue characteristics for chemotherapy-induced myelosuppression in patients with lung cancer.

This study aimed to investigate the clinical predictors, including traditional Chinese medicine tongue characteristics and other clinical parameters for chemotherapy-induced myelosuppression (CIM), and then to develop a clinical prediction model and construct a nomogram. A total of 103 patients with lung cancer were prospectively enrolled in this study. All of them were scheduled to receive first-line chemotherapy regimens. Participants were randomly assigned to either the training group (n = 52) or the test group (n = 51). Tongue characteristics and clinical parameters were collected before the start of chemotherapy, and then the incidence of myelosuppression was assessed after treatment. We used univariate logistic regression analysis to identify the risk predictors for assessing the incidence of CIM. Moreover, we developed a predictive model and a nomogram using multivariate logistic regression analysis. Finally, we evaluated the predictive performance of the model by examining the area under the curve value of the receiver operating characteristic, calibration curve, and decision curve analysis. As a result, a total of 3 independent predictors were found to be associated with the CIM in multivariate regression analysis: the fat tongue (OR = 3.67), Karnofsky performance status score (OR = 0.11), and the number of high-toxic drugs in chemotherapy regimens (OR = 4.78). Then a model was constructed using these 3 predictors and it exhibited a robust predictive performance with an area under the curve of 0.82 and the consistent calibration curves. Besides, the decision curve analysis results suggested that applying this predictive model can result in more net clinical benefit for patients. We established a traditional Chinese medicine prediction model based on the tongue characteristics and clinical parameters, which could serve as a useful tool for assessing the risk of CIM.