Inhibiting L1CAM Reverses Cisplatin Resistance of Triple Negative Breast Cancer Cells by Blocking AKT Signaling Pathway

Extracellular vesicles from UTX-knockout endothelial cells boost neural stem cell differentiation in spinal cord injury

BACKGROUND

Vascular endothelial cells are pivotal in the pathophysiological progression following spinal cord injury (SCI). The UTX (Ubiquitously Transcribed Tetratripeptide Repeat on Chromosome X) serves as a significant regulator of endothelial cell phenotype. The manipulation of endogenous neural stem cells (NSCs) offers a compelling strategy for the amelioration of SCI.

METHODS

Two mouse models were used to investigate SCI: NSCs lineage-traced mice and mice with conditional UTX knockout (UTX KO) in endothelial cells. To study the effects of UTX KO on neural differentiation, we harvested extracellular vesicles (EVs) from both UTX KO spinal cord microvascular endothelial cells (SCMECs) and negative control SCMECs. These EVs were then employed to modulate the differentiation trajectory of endogenous NSCs in the SCI model.

RESULTS

In our NSCs lineage-traced mice model of SCI, a marked decrease in neurogenesis was observed post-injury. Notably, NSCs in UTX KO SCMECs mice showed enhanced neuronal differentiation compared to controls. RNA sequencing and western blot analyses revealed an upregulation of L1 cell adhesion molecule (L1CAM), a gene associated with neurogenesis, in UTX KO SCMECs and their secreted EVs. This aligns with the observed promotion of neurogenesis in UTX KO conditions. In vivo administration of L1CAM-rich EVs from UTX KO SCMECs (KO EVs) to the mice significantly enhanced neural differentiation. Similarly, in vitro exposure of NSCs to KO EVs resulted in increased activation of the Akt signaling pathway, further promoting neural differentiation. Conversely, inhibiting Akt phosphorylation or knocking down L1CAM negated the beneficial effects of KO EVs on NSC neuronal differentiation.

CONCLUSIONS

In conclusion, our findings substantiate that EVs derived from UTX KO SCMECs can act as facilitators of neural differentiation following SCI. This study not only elucidates a novel mechanism but also opens new horizons for therapeutic interventions in the treatment of SCI. Video Abstract.

Integrated Bioinformatics Analysis to Identify a Novel Four-Gene Prognostic Model of Breast Cancer and Reveal Its Association with Immune Infiltration

Liquid-liquid phase separation (LLPS) impact immune signaling in cancer and related genes have shown prognostic value in breast cancer (BRCA). However, the crosstalk between LLPS and immune infiltration in BRCA remain unclear. Therefore, we aimed to develop a novel prognostic model of BRCA related to LLPS and immune infiltration. BRCA-related, liquid-liquid phase separation (LLPS)-related genes, and differentially expressed genes (DEGs) were identified using public databases. Mutation and drug sensitivity analyses were performed using Gene Set Cancer Analysis database. Univariate cox regression and LASSO Cox regression were used for the construction and verification of prognostic model. Kaplan-Meier analysis was performed to evaluate overall survival (OS). Gene set variation analysis was conducted to analyze key pathways. CIBERSORT was used to assess immune infiltration and its correlation with prognostic genes was determined through Pearson analysis. A total of 6056 BRCA-associated genes, 3775 LLPS-associated genes, and 4049 DEGs, resulting in 314 overlapping genes. Twenty-eight prognostic genes were screened, and some of them were mutational and related to drug sensitivity Subsequently, a prognostic model comprising L1CAM, EVL, FABP7, and CST1 was built. Patients in high-risk group had shorter OS than those in low-risk group. The infiltrating levels of CD8+ T cells, macrophages M0, macrophages M2, dendritic cells activated, and mast cells resting was altered in high-risk group of breast cancer patients compared to low-risk group. L1CAM, EVL, FABP7, and CST1 were related to these infiltrating immune cells. L1CAM, EVL, FABP7, and CST1 were potential diagnostic biomarkers and therapeutic targets for BRCA.

Development of a circHIPK3-based ceRNA network and identification of mRNA signature in breast cancer patients harboring BRCA mutation

Background

Exploring the regulatory network of competing endogenous RNAs (ceRNAs) as hallmarks for breast cancer development has great significance and could provide therapeutic targets. An mRNA signature predictive of prognosis and therapy response in BRCA carriers was developed according to circular RNA homeodomain-interacting protein kinase 3 (circHIPK3)-based ceRNA network.

Method

We constructed a circHIPK3-based ceRNA network based on GSE173766 dataset and identified potential mRNAs that were associated with BRCA mutation patients within this ceRNA network. A total of 11 prognostic mRNAs and a risk model were identified and developed by univariate Cox regression analysis and the LASSO regression analysis as well as stepAIC method. Genomic landscape was treated by mutect2 and fisher. Immune characteristics was analyzed by ESTIMATE, MCP-counter. TIDE analysis was conducted to predict immunotherapy. The clinical treatment outcomes of BRCA mutation patients were assessed using a nomogram. The proliferation, migration and invasion in breast cancer cell lines were examined using CCK8 assay and transwell assay.

Result

We found 241 mRNAs within the circHIPK3-based ceRNA network. An 11 mRNA-based signature was identified for prognostic model construction. High risk patients exhibited dismal prognosis, low response to immunotherapy, less immune cell infiltration and tumor mutation burden (TMB). High-risk patients were sensitive to six anti-tumor drugs, while low-risk patient were sensitive to 47 drugs. The risk score was the most effective on evaluating patients' survival. The robustness and good prediction performance were validated in The Cancer Genome Atlas (TCGA) dataset and immunotherapy datasets, respectively. In addition, circHIPK3 mRNA level was upregulated, and promoted cell viability, migration and invasion in breast cancer cell lines.

Conclusion

The current study could improve the understanding of mRNAs in relation to BRCA mutation and pave the way to develop mRNA-based therapeutic targets for breast cancer patients with BRCA mutation.

A gene expression signature in HER2+ breast cancer patients related to neoadjuvant chemotherapy resistance, overall survival, and disease-free survival

Breast cancer ranks first in terms of mortality and incidence rates worldwide among women. The HER2+ molecular subtype is one of the most aggressive subtypes; its treatment includes neoadjuvant chemotherapy and the use of a HER2 antibody. Some patients develop resistance despite positive results obtained using this therapeutic strategy. Objective. To identify prognostic markers for treatment and survival in HER2+ patients. Methods. Patients treated with neoadjuvant chemotherapy were assigned to sensitive and resistant groups based on their treatment response. Differentially expressed genes (DEGs) were identified using RNA-seq analysis. KEGG pathway, gene ontology, and interactome analyses were performed for all DEGs. An enrichment analysis Gene set enrichment analysis was performed. All DEGs were analyzed for overall (OS) and disease-free survival (DFS). Results. A total of 94 DEGs were related to treatment resistance. Survival analysis showed that 12 genes (ATF6B, DHRS13, DIRAS1, ERAL1, GRIN2B, L1CAM, IRX3, PRTFDC1, PBX2, S100B, SLC9A3R2, and TNXB) were good predictors of disease-free survival, and eight genes (GNG4, IL22RA2, MICA, S100B, SERPINF2, HLA-A, DIRAS1, and TNXB) were good predictors of overall survival (OS). Conclusion: We highlighted a molecular expression signature that can differentiate the treatment response, overall survival, and DFS of patients with HER2+ breast cancer.