Investigation of olfactory receptor family 51 subfamily j member 1 (OR51J1) gene susceptibility as a potential breast cancer-associated biomarker.
Maryam AsadiNahid AhmadiSimin AhmadvandAli Akbar JafariAkbar SafaeiNasrollah ErfaniAmin RamezaniPublished in: PloS one (2021)
Among cancer treatment methods, targeted therapy using cancer-associated biomarkers has minimum side effects. Recently olfactory receptor (OR) family attracts the researcher's attention as a favorable biomarker of cancer. Here, a statistical approach using complete data from the human protein atlas database was used to evaluate the potential of OR51J1 gene as a cancer-associated biomarker. To confirm the findings of statistical analysis, the OR51J1 mRNA and protein expression levels in breast tumor and normal tissue were measured using quantitative Real Time PCR (qRT-PCR) and immunohistochemistry (IHC) techniques. The association with clinicopathological factors was analyzed. Statistical analysis revealed that OR51J1 has a high expression level in more than 20 types of cancer tissues without any expression in 44 normal tissues. In 15 cancer types, including breast cancer, expression score was more than 90%. The qRT-PCR analysis in breast cancer showed OR51J1 have significantly higher expression level in tumors than normal tissues (2.91 fold). The IHC results showed OR51J1 expression on other cellular subtypes than tumor and normal cells, including myoepithelium, fibroblast, and lymphocytes. OR51J1 protein expression in invasive cells, as well as its overall score, showed a significant correlation with ER and PR expression and breast cancer (BC) subtypes. Results revealed the potential of OR51J1 as a cancer-associated biomarker for the diagnosis of breast cancer at the mRNA level.
Keyphrases
- poor prognosis
- binding protein
- gene expression
- induced apoptosis
- papillary thyroid
- real time pcr
- long non coding rna
- endothelial cells
- copy number
- genome wide
- oxidative stress
- high resolution
- human health
- endoplasmic reticulum stress
- machine learning
- big data
- transcription factor
- induced pluripotent stem cells
- protein protein