Distinct Classes of Flavonoids and Epigallocatechin Gallate, Polyphenol Affects an Oncogenic Mutant p53 Protein, Cell Growth and Invasion in a TNBC Breast Cancer Cell Line

Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure.

Method

Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes.

Result

TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response.

Conclusion

In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.

p53 downregulates PD-L1 expression via miR-34a to inhibit the growth of triple-negative breast cancer cells: a potential clinical immunotherapeutic target

BACKGROUND

Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) has poorer responses to therapy and lower overall survival rates. The use of an inhibitor of immune checkpoint programmed cell death ligand 1 (PD-L1) is a promising treatment strategy and is approved for malignant tumors, especially for TNBC. p53 regulates various biological processes, but the association between p53 and immune evasion remains unknown. miR-34a is a known tumor suppressor and p53-regulated miRNA that is downregulated in several cancers; however, it has not been reported in TNBC. Herein, we aimed to explore the regulatory signaling axis among p53, miR-34a and PD-L1 in TNBC cells in vivo and in tissue and to improve our understanding of immunotherapy for TNBC.

METHODS AND RESULTS

p53-EGFP, p53-siRNA and miR-34a mimics were transfected into TNBC cell lines, and the interaction between miR-34a and PD-L1 was analyzed via dual-luciferase reporter assays. We found that p53 could inhibit the expression of PD-L1 via miR-34a and that miR-34a could inhibit both cell activity and migration and promoted apoptosis and cytotoxicity in TNBC. Furthermore, miR-34a agomir was injected into MDA-MB-231 tumors of nude mice. The results showed that miR-34a could inhibit tumor growth and downregulate the expression of PD-L1 in vivo. A total of 133 TNBC tissue samples were analyzed by immunochemistry; the proportion of positive expression of PD-L1 was 57.14% (76/133), and the proportion of samples with negative expression of PD-L1 was 42.86% (57/133).

CONCLUSION

Our research may provide a novel potential target for TNBC.