Differential Expression and Function of SVIP in Breast Cancer Cell Lines and In Silico Analysis of Its Expression and...

The heterogeneity of cancer strongly suggests the need to explore additional pathways to target. As cancer cells have increased proteotoxic stress, targeting proteotoxic stress-related pathways such as endoplasmic reticulum stress is attracting attention as a new anticancer treatment. One of the downstream responses to endoplasmic reticulum stress is endoplasmic reticulum-associated degradation (ERAD), a major degradation pathway that facilitates proteasome-dependent degradation of unfolded or misfolded proteins. Recently, SVIP (small VCP/97-interacting protein), an endogenous ERAD inhibitor, has been implicated in cancer progression, especially in glioma, prostate, and head and neck cancers. Here, the data of several RNA-sequencing (RNA-seq) and gene array studies were combined to evaluate the SVIP gene expression analysis on a variety of cancers, with a particular focus on breast cancer. The mRNA level of SVIP was found to be significantly higher in primary breast tumors and correlated well with its promoter methylation status and genetic alterations. Strikingly, the SVIP protein level was found to be low despite increased mRNA levels in breast tumors compared to normal tissues. On the other hand, the immunoblotting analysis showed that the expression of SVIP protein was significantly higher in breast cancer cell lines compared to non-tumorigenic epithelial cell lines, while most of the key proteins of gp78-mediated ERAD did not exhibit such an expression pattern, except for Hrd1. Silencing of SVIP enhanced the proliferation of p53 wt MCF-7 and ZR-75-1 cells but not p53 mutant T47D and SK-BR-3 cells; however, it increased the migration ability of both types of cell lines. Importantly, our data suggest that SVIP may increase p53 protein levels in MCF7 cells by inhibiting Hrd1-mediated p53 degradation. Overall, our data reveal the differential expression and function of SVIP on breast cancer cell lines together with in silico data analysis.