New Gene Profiling in Determination of Breast Cancer Recurrence and Prognosis in Iranian Women


Department of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran E-mail:


Breast cancer (BC) is the second most common cancer in the world and by far the most frequent cancer among women, with an estimated 1.67 million new cancer cases diagnosed in 2012 (25% of all cancers). Polygene expression analysis is used to predict the prognosis and determine the most appropriate treatment regimen. The objective of this study was to examine the gene expression profiles of SIRT3, HRAS, LSP1, SCUBE2 and AP2A2 in Iranian women with BC.A total of 136 patients including healthy controls were categorized into three groups based on the relapse of the disease. Expression of desired genes in formalin-fixed, paraffin embedded tissues collected from all groups of participants was analyzed via the RT PCR method. RNA extraction and cDNA synthesis were performed then real-time quantitative PCR was carried out. Gene expression analysis revealed that the expression of SIRT3 was equal among patient and control groups. LSP1 was down regulated in all patient groups relative to controls but reduced expression in the metastatic group relative to the non-metastatic one was not significant. HRAS was significantly overexpressed in total and metastatic tumor samples versus normal but not in non-metastatic cases. SCUBE2 expression showed significant over-expression in both overall tumor samples and the non-metastatic group as compared to normal tissues. Gene expression level of AP2A2 in all groups was not detectable. Our data are compatible with a tumor suppressor role of LSP1 related to potential prognostic factor for tumor recurrence and outcome. This study for the first time assayed the prognostic value and changes in the expression of SIRT3, LSP1, HRAS, SCUBE2 and AP2A2 genes in women with breast cancer in the Iranian population and findings confirmed potential biomarker and prognostic capability of these genes. Such expression profiling data can critically improve prognosis and treatment decisions in cancer patients.