CinéBreast-factors influencing the time to first metastatic recurrence in breast cancer: Analysis of real-life data from the French ESME MBC database

Combined mRNAs and clinical factors model on predicting prognosis in patients with triple-negative breast cancer

OBJECTIVE

Triple-negative breast cancer (TNBC) is aggressive cancer usually diagnosed in young women with no effective prognosis prediction model to use. The present study was performed to develop a useful prognostic model for predicting overall survival (OS) for TNBC patients.

METHODS

The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used as training and validation data sets, respectively, in which the gene expression levels and clinical prognostic information of TNBC were collected. Differentially expressed genes (DEGs) between TNBC and non-TNBC (NTNBC) were identified with the thresholds of false discovery rate < 0.05 and |log2 Fold Change| > 1. DEGs in AmiGO2 and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were retained for further study. Univariate, multivariate Cox, and logistic regression analysis were conducted for detecting DEG signature with the threshold of log-rank P < 0.05. The prognosis models of mRNA signature, clinical factors were constructed and compared.

RESULTS

One five-DEG signature, including CHST4, COCH, CST9, SOX11, and TDGF1 was identified in DEG prognosis model. Stratified analysis showed that the patients aged over 60, with higher pathologic stage (III-IV) and recurrence induced a significantly lower survival rate than those aged below 60, lower pathologic stage and without recurrence. Compared with patients with low-risk scores, those presented high-risk scores demonstrated significantly lower survival rate in the subgroup aged over 60 [HR = 3.780 (1.801-7.933), P < 0.0001]. For patients who obtained a higher pathologic stage and recurrence, high-risk scores were correlated with a significantly lower survival rate than patients with low-risk scores. The five-mRNA signature combined with clinical model (AUC = 0.950) predicted better than single clinical model (AUC = 0.795) or five-mRNA signature model (AUC = 0.823).

CONCLUSION

Our present study identified a prognostic prediction model (combined with five-mRNA signature and clinical factors) for TNBC patients receiving immunotherapy, which will benefit future research and clinical therapies.

C1orf63 silencing affects breast cancer cell proliferation, apoptosis, and cycle distribution by NF-κB signaling pathway

OBJECTIVE

To investigate the effect of C1orf63 on breast cancer cell (BCC) proliferation, apoptosis, and cycle distribution and related mechanisms.

METHODS

The expression of C1orf63 was interfered with in BCC line MCF and cells were divided into a C1orf63 overexpression group, C1orf63 silence group, blank group, and empty group. The mRNA expression of C1orf63 and the proliferation, apoptosis, and cycle distribution of BCCs were detected. The mRNA expression levels of NF-κB signaling pathway factors (p-IκBα, CyclinD1, CDK4, Bcl-2, and Bax) in each group were also detected.

RESULTS

There was no significant difference between the blank group and empty group in the expression level of C1orf63 mRNA, cell proliferation rate, apoptosis rate, cell distribution rate, or mRNA expression levels of the NF-κB signaling pathway factors (all P>0.05). The expression levels of C1orf63 mRNA in the C1orf63 silenced group were lower than those in the other two groups (P<0.05). The cell proliferation rate, cell distribution in S phase and G2/M phase, and the mRNA expression levels of NF-κB signaling pathway factors (p-IκBα, CyclinD1, CDK4, and Bcl-2) in the C1orf63 silenced group at each time point were lower than those in the other two groups (all P<0.05). The apoptosis rate, cells in G1 phase, and the Bax mRNA expression level in C1orf63 silenced group at each time point were higher than those in the other two groups (all P<0.05).

CONCLUSION

Down-regulation of C1orf63 acts on the NF-κB signaling pathway to regulate the expression of p-IκBα, CyclinD1, and CDK4, so as to inhibit BCC proliferation, promote cell apoptosis, and block the cell cycle.