Identification of Subtypes in Triple-negative Breast Cancer Based on Shared Genes Between Immunity and Cancer Stemness.

The correlation between triple-negative breast cancer (TNBC) and genes related to immunity and cancer stemness, particularly shared genes, remains unclear. This study aimed to investigate the correlation of immunity and cancer stemness with the molecular subtyping and survival rates in TNBC using bioinformatics approaches. Differential gene analysis was conducted to identify TNBC-associated differentially expressed genes (DEGs). Cancer stem cell (CSC)-related genes were obtained using weighted gene coexpression network analysis. Immune-related gene sets were retrieved from the literature. Venn analysis was performed to identify the shared DEGs between immunity and cancer stemness in TNBC. Cluster analysis and survival analysis based on the expression of these genes were conducted to identify TNBC subtypes with significant survival differences. A total of 5259 TNBC-associated DEGs, 2214 CSC-related genes, 1793 immune-related genes, and 44 shared DEGs between immunity and cancer stemness were obtained. Among them, 3 shared DEGs were closely associated with TNBC survival rates ( P <0.05). Cluster and survival analyses revealed that among 3 subtypes, cluster2 exhibited the best survival rate, and cluster3 showed the worst survival rate ( P <0.05). Dendritic cells were highly infiltrated in cluster2, while plasma cells and resting mast cells were highly infiltrated in cluster3 ( P <0.05). Genes shared by immunity and cancer stemness were capable of classifying TNBC samples. TNBC patients of different subtypes exhibited significant differences in immune profiles, genetic mutations, and drug sensitivity. These findings could provide new insights into the pathogenesis of TNBC, the immune microenvironment, and the selection of therapeutic targets for drug treatment.

Palbociclib sensitizes ER-positive breast cancer cells to fulvestrant by promoting the ubiquitin-mediated degradation of ER-α via SNHG17/Hippo-YAP axis.

PURPOSE: Endocrine therapy is the anti-tumor therapy for human breast cancer but endocrine resistance was a major burden. It has been reported that Palbociclib and fulvestrant can be used in combination for the treatment of patients who are experiencing endocrine resistance. However, the underlying mechanism is unclear. In this study, we aimed to investigate the mechanism by which Palbocicilib affected ER-positive breast cancer, combined with fulvestrant. METHODS: We first detected the effect of palbociclib on cell survival, growth and cycle distribution separately by MTT, colony formation and flow cytometry. Then SNHG17 was screened as palbociclib-targeted LncRNA by LncRNA-seq, and the SNHG17-targeted mRNAs were selected by mRNA-seq for further determination. Subsequently, the underlying mechanism by which palbociclib promoted the cytotoxicity of fulvestrant was confirmed by qRT-PCR, western blot, and immunoprecipitation. Eventually, the xenograft model and immunohistochemistry experiments were used to validate the sensitization effect of palbociclib on fulvestrant and its mechanism in vivo. RESULTS: Palbociclib significantly enhanced the cytotoxicity of fulvestrant in fulvestrant-resistant breast cancer cell lines. Interestingly, this might be related to the lncRNA SNHG17 and the Hippo signaling pathway. And our subsequent western blotting experiments confirmed that overexpressing SNHG17 induced the down-regulation of LATS1 and up-regulated YAP expression. Furthermore, we found that the increased sensitivity of breast cancer cells was closely associated with the LATS1-mediated degradation of ER-α. The following animal experiments also indicated that overexpressing SNHG17 obviously impaired the anti-cancer effect of co-treatment of palbociclib and fulvestrant accompanied by decreased LATS1 and increased ER-α levels. CONCLUSION: Palbociclib might sensitize the cytotoxicity of fulvestrant in ER-positive breast cancer cells by down-regulating SNHG17 expression, and then resulted in the LATS1-inactivated oncogene YAP and LATS1-mediated degradation of ER-α.

Matrine exerts an anti-tumor effect via regulating HN1 in triple breast cancer both in vitro and in vivo.

The treatment of triple-negative breast cancer (TNBC) cannot meet medical needs, and it is urgent to find new drugs for intervention. This study aimed to investigate the anti-tumor effect of matrine on the proliferation and apoptosis of TNBC cells based on HN1 regulation in vitro and in vivo. TNBC cell lines (MDA-MB-453 and HCC-1806) were treated with varying concentrations of matrine (0, 1.0, 2.0, 3.0, 4.0, and 5.0 mM). CCK-8, colony formation assay, transwell assay, and flow cytometry assay were employed to detect proliferation, clone formation, invasion, and apoptosis of TNBC cells. Western blot analysis was applied to detect the protein expression of apoptosis HN1. The effects of matrine on tumor growth, protein expression of HN1, and apoptosis in vivo were validated by xenograft tumor models and histology. It was found that matrine inhibited proliferation, colony formation, and invasion and promoted apoptosis of TNBC cells in vitro. HN1 expression was suppressed by matrine. HN1 overexpression perceptibly reversed the above-mentioned additive effect in vitro. In vivo experiments found that matrine inhibited tumor growth and the expression of HN1 protein but promoted the protein expression of Cleared-Caspase-3. Above all, this study demonstrated that matrine inhibited proliferation and promoted apoptosis of TNBC cells via suppressing HN1 expression. Targeting HN1 by matrine may provide new insights into the therapeutic management of patients with TNBC.

Construction of a four-mRNA prognostic signature with its ceRNA network in CESC.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC. In this study, the RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas. Differentially expressed mRNAs, lncRNAs and miRNAs were identified with the edgeR R package. A four-mRNA prognostic signature was developed by multivariate Cox regression analysis. Kaplan-Meier survival with the log-rank tests was performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed with the multiMiR package and miRcode database. Kyoto encyclopedia of genes and genomes (KEGG) analysis and gene ontology analyses were used to identify the functional role of the ceRNA network in the prognosis of CESC. A total of 298 differentially expressed mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. A prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was developed, and the prognostic ability of this signature was indicated by the AUC of 0.726. Patients in the high-risk group exhibited significantly worse OS. The KEGG pathways, TGF-ß and Cell adhesion molecules, were significantly enriched. In this study, a CESC-associated ceRNA network was constructed, and a multi-mRNA-based prognostic model for CESC was developed based on the ceRNA network, providing a new perspective for cancer pathogenesis research.