Abstract
Breast cancer is a heterogeneous disease associated with diverse clinical, biological and molecular features, presenting huge challenges for prognosis and treatment. Here we found that perilipin-1 (PLIN1) mRNA expression is significantly downregulated in human breast cancer. Kaplan-Meier analysis indicated that patients presenting with reduced PLIN1 expression exhibited poorer overall metastatic relapse-free survival (p = 0.03). Further Cox proportional hazard models analysis revealed that the reduced expression of PLIN1 is an independent predictor of overall survival in estrogen receptor positive (p < 0.0001, HR = 0.87, 95% CI = 0.81–0.92, N = 3,600) and luminal A-subtype (p = 0.02, HR = 0.88, 95% CI = 0.78–0.98, N = 1,469) breast cancer patients. We also demonstrated that the exogenous expression of PLIN1 in human breast cancer MCF-7 and MDA-MB-231 cells significantly inhibits cell proliferation, migration, invasion and in vivo tumorigenesis in mice. Together, these data provide novel insights into a prognostic significance of PLIN1 in human breast cancer and reveal a potentially new gene therapy target for breast cancer.
Highlights
Breast cancer is the most common malignancy among women (i.e., 200,000 new cases diagnosed each year in the United States) and represents an important worldwide public health issue [1, 2]
We identified 57 genes that exhibit deregulated expression patterns in breast cancer using patient expression data obtained from the the Cancer Genome Atlas (TCGA) database
Our results indicate that patients with low PLIN1 mRNA levels have decreased overall and metastatic relapse (MR)-free survival time, in estrogen receptor (ER)-positive and luminal A subtype patients
Summary
Breast cancer is the most common malignancy among women (i.e., 200,000 new cases diagnosed each year in the United States) and represents an important worldwide public health issue [1, 2]. The treatment of breast cancer is difficult in patients with metastatic tumors [3]. Progress has been made in the diagnosis and treatment of breast cancer, the prognosis and survival for most patients, those with metastases, have not dramatically improved [4, 5]. There is an urgent need for the identification of diagnostic markers and potential cellular and molecular mechanisms underlying tumor metastasis, as well as for the development of new therapeutic strategies for improving patient survival and overall quality of life. The generations of publicly available large-scale datasets, such as the Cancer Genome Atlas (TCGA), provide comprehensive catalogs of multiple data types performed on the same set of samples. Various groups www.impactjournals.com/oncotarget have identified large multi-gene signatures that are prognostic for outcomes in molecularly profiled human breast cancer samples through the TCGA database [9,10,11]
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