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GenesWell BCT 임상적 검증
한국인 검체를 대상으로 GenesWell BCT의 임상적 성능 검증
A new molecular prognostic score for predicting the risk of distant metastasis in patients with HR+/HER2- early breast cancer Gyungyub Gong, Mi Jeong Kwon, Jinil Han, Hee Jin Lee, Se Kyung Lee, Jeong Eon Lee, Seon-Heui Lee, Sarah Park, Jong-Sun Choi, Soo Youn Cho, Sei Hyun Ahn, Jong Won Lee, Sang Rae Cho, Youngho Moon, Byung-Ho Nam, Seok Jin Nam, Yoon-La Choi, Young Kee Shin Scientific Reports| 2017
To make an optimal treatment decision for early stage breast cancer, it is important to identify risk of recurrence. Here, we developed and validated a new prognostic model for predicting the risk of distant metastasis in patients with pN0-N1, hormone receptor-positive, HER2-negative (HR+/HER2−) breast cancer treated with hormone therapy alone. RNA was extracted from formalin-fixed, paraffin-embedded tumor tissues and gene expression was measured by quantitative real-time reverse transcription-PCR. The relative expression of six novel prognostic genes was combined with two clinical variables (nodal status and tumor size) to calculate a risk score (BCT score). In the validation cohort treated with hormone therapy alone, the 10 year rate of distant metastasis in the high-risk group (26.3%) according to BCT score was significantly higher than that in the low-risk group (3.8%) (P < 0.001). Multivariate analysis adjusted for clinical variables revealed that BCT score is an independent predictor of distant metastasis. Moreover, the C-index estimate revealed that BCT score has a prognostic power superior to that of prognostic models based on clinicopathological parameters. The BCT score outperforms prognostic models based on traditional clinicopathological factors and predicts the risk of distant metastasis in patients with HR+/HER2− early breast cancer.
기존의 전통적인 표준유전자의 단점을 보완한 신규 내인성 표준 유전자 발굴
Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis Mi Jeong Kwon, Ensel Oh, Seungmook Lee, Mi Ra Roh, Si Eun Kim, Yangsoon Lee, Yoon-La Choi, Yong-Ho In, Taesung Park, Sang Seok Koh, Young Kee Shin PLoS ONE | 2009
Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative realtime RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.
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A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity Ensel Oh, Yoon-La Choi, Taesung Park, Seungyeoun Lee, Seok Jin Nam, Young Kee Shin Breast Cancer Res Treat | 2011
A model for a more precise prognosis of the risk of relapse is needed to avoid overtreatment of lymph node-negative breast cancer patients. A large derivation data set (n = 684) was generated by pooling three independent breast cancer expression microarray data sets. Two major prognostic factors, proliferation and immune response, were identified among genes showing significant differential expression levels between the good outcome and poor outcome groups. For each factor, four proliferation-related genes (p-genes) and four immunity-related genes (i-genes) were selected as prognostic genes,