bioRxivMolecular BiologyPreprint
Exploring the metastatic role of the inhibitor of apoptosis BIRC6 in Breast Cancer
Santiago M. Gómez BergnaiD, Abril MarchesiniiD, Leslie C. Amorós MoralesiD, Paula Nazarena ArríasiD, Hernán G. FariñaiD, Vı́ctor RomanowskiiD, María Florencia GottardoiD, Matías L. PidreiD
Abstract
Breast cancer is the most common cancer as well as the first cause of death by cancer in women worldwide. BIRC6 (baculoviral IAP repeat-containing protein 6) is a member of the inhibitors of apoptosis protein family thought to play an important role in the progression or chemoresistance of many cancers. The aim of the present work was to investigate the role of apoptosis inhibitor BIRC6 in breast cancer, focusing particularly on its involvement in the metastatic cascade. We analyzed BIRC6 mRNA expression levels and Copy Number Variations (CNV) in three breast cancer databases from The Cancer Genome Atlas (TCGA) comparing clinical and molecular attributes. Genomic analysis was performed using CBioportal platform while transcriptomic studies (mRNA expression levels, correlation heatmaps, survival plots and Gene Ontology) were performed with USC Xena and R. Statistical significance was set at p-values less than 0.05. Our analyses showed that there was a differential expression of BIRC6 in cancer samples when compared to normal samples. CNV that involve amplification and gain of BIRC6 gene were correlated with negative hormone receptor tumors, higher prognostic indexes, younger age at diagnosis and both chemotherapy and radiotherapy administration. Transcriptomic and gene-ontology analyses showed that, in conditions of high BIRC6 mRNA levels, there are differential expression patterns in apoptotic, proliferation, and metastatic pathways. In summary, our in silico analyses suggest that BIRC6 exhibits an antiapoptotic, pro-proliferative and an apparent pro-metastatic role and could be a relevant molecular target for treatment of Breast Cancer tumors.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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