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Gene Expression–Based Predictors of Prognosis and Response to Chemotherapy in Breast Cancer
Published in Brian Leyland-Jones, Pharmacogenetics of Breast Cancer, 2020
Correlating clinical outcome with expression levels yielded many genes from these three studies, and 16 top-performing genes were identified for final model building and validation. Relative expression levels of the 16 genes are measured in relationship to average expression levels of 5 reference genes. While a majority of genes comprising 16 genes are ER (ER, PGR, BCL2, SCUBE2) and proliferation (Ki67, STK15, Survivin, CCNB1, MYBL2) related, there are other genes (HER2, GRB7, MMP11, CTSL2, GSTM1, CD68, BACG1) (9). The unscaled recurrence score (RSu) was calculated with the use of coefficient that are defined on the basis of regression analysis of gene expression. Recurrence score (RS) was rescaled from the unscaled recurrence score (Rsu) as follows: RS = 0 if Rsu < 0; RS = 20 × (Rsu–6.7) if 0 ≤ Rsu ≤ 100; and RS = 100 if Rsu > 100 (9). This resulted in RS ranging from 0 to 100.
Great strides in precision medicine: Personalized oncology and molecular diagnostics
Published in Priya Hays, Advancing Healthcare Through Personalized Medicine, 2017
The Oncotype DX platform utilizes quantitative real-time PCR (RT-qPCR) to analyze the expression of 16 cancer-related genes: Ki67, STK15, survivin, CCNB1, MYBL2, GRB7, HER2, ER, PGR, BCL2, SCUBE2, MMP11, CTSL2, GSTM1, CD68, and BAG1, in addition to five reference genes (ACTB, GAPDH, RPLPO, GUS, and TFRC). Oncotype DX predicts risk of recurrence in ER-positive, lymph node-negative breast cancer patients and provides treatment guidelines via a quantitative system of continuous recurrence score (RS) that ranges between 0 and 100 to predict the risk of recurrence within 10 years. This score has also been shown to be an independent predictor of outcome in multivariate survival analyses. The test utilizes RNA extracted from formalin-fixed paraffin-embedded ER-positive breast cancer samples.
A novel DNA methylation 10-CpG prognostic signature of disease-free survival reveal that MYBL2 is associated with high risk in prostate cancer
Published in Expert Review of Anticancer Therapy, 2020
Xueying Hou, Yuelin Zhang, Siyuan Han, Baoxian Hou
The Cancer Genome Atlas (TCGA) database contains multi-omics data on a variety of cancers, and its large sample size is useful in exploring the underlying molecular mechanisms of tumors [8]. In this study, data from the TCGA database were used to develop a DNA methylation 10-CpG prognostic signature which could indicate the prognosis of PC patients. Weighted correlation network analysis (WGCNA) aims to find the gene modules in which the RNA levels of genes are correlated and to explore the association between gene modules and phenotypes of samples [9,10]. Each patient’s LASSO score (indicating the risk of patients) was calculated using our DNA methylation prognostic signature, and then we used the WGCNA algorithm to screen out the gene module most relevant to the LASSO score. In this gene module, we found that MYBL2 had not been reported and was significantly highly expressed at both RNA and protein levels in PC. After functional verification, we found that MYBL2 could facilitate the proliferation, migration, invasion, and metastasis of PC cell in vitro.