An individualized transcriptional signature to predict the epithelial-mesenchymal transition based on relative expression ordering

• DOI: 10.18632/aging.103407
• Publication Date: 2020-07-08T19:35:36Z
• Authors: Tingting Chen; Zhangxiang Zhao; Bo Chen; Yuquan Wang; Fan Yang; Chengyu Wang; Qi Dong; Yaoyao Liu; Haihai Liang; Wenyuan Zhao; Lishuang Qi; Yan Xu; Yunyan Gu
• Keywords: Signature (topology); Expression (computer science)

The epithelial-mesenchymal transition (EMT) process is involved in cancer cell metastasis and immune system activation. Hence, identification of gene expression signatures capable predicting the EMT status cells essential for development therapeutic strategies. However, quantitative markers limited by batch effects, platform used, or normalization methods. We hypothesized that a set EMT-related relative orderings are highly stable epithelial samples yet reversed mesenchymal samples. To test this hypothesis, we analyzed transcriptome data ovarian cohorts from publicly available databases, to develop qualitative 16-gene pair signature (16-GPS) effectively distinguishes phenotype. Our method was superior previous methods terms classification accuracy applicability individualized patients without requiring normalization. Patients with mesenchymal-like showed poorer overall survival compared epithelial-like cancer. Additionally, score positively correlated checkpoint genes metastasis. We, therefore, established robust 16-GPS independent detection platform, effects individual variations, which represents investigating providing insights into immunotherapy patients.