bioRxivCancer BiologyPreprint
Upregulation of GREB1 in colorectal cancer ovarian metastases may be a potential therapeutic target
Samantha M. RuffiD, Alessandro La FerlitaiD, Manoj H. Palavalli, Joal D. BeaneiD, Susan Tsai, Oliver S. EngiD, Alex C. KimiD, Ashwini Kumar EsnakulaiD
Abstract
IntroductionClassification of ovarian metastases (OM) in colorectal cancer (CRC) as peritoneal metastasis (PM) remains controversial. OM demonstrate resistance to systemic therapy, suggesting distinct molecular tumorigenesis. Gene expression profiling and transcriptomic analysis were performed to distinguish OM, PM, and primary CRC (pCRC) and identify potential therapeutic targets. MethodsRNA sequencing data were obtained from the Total Cancer Care database. After filtering out low-expressed genes, raw counts of retained genes were normalized in trimmed mean of M-values and then log2-transformed. Genes with a |Log2FC|>0.58 and adjusted p-value <0.05 were considered dysregulated. In silico motif enrichment analysis of estrogen receptor 1 (ESR1) on growth regulating estrogen receptor binding 1 (GREB1) was conducted. ResultsThere were 115 patients with tissue from OM (n=6), PM (n=4), and pCRC (n=105). Among upregulated genes, LAMC3, SCUBE1, and GREB1 had the highest differential expression in OM compared to PM, and PEG3, C7, and GREB1 had the highest differential expression in OM compared to pCRC (all p<0.001). GREB1 was upregulated in OM compared to PM and pCRC. There are two estrogen response element (ERE) sites within the promoter region of GREB1. ESR1 transcription factor was shown to bind to these ERE (p<0.001). ConclusionsTranscriptomic analysis demonstrated clear molecular distinction between OM, PM and pCRC. We identified upregulation of GREB1 in OM compared to PM and pCRC. GREB1 contains both ERE and ESR1 in the upstream promoter regions implying potential upstream transcription regulation mediated by ESR1. GREB1 may represent a novel, hormonal target in treatment of OM in CRC.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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