Xiaolu Zhang1, Jia Wang2, Jing Zhuang3, Cun Liu1, Chundi Gao1, Huayao Li2, Xiaoran Ma1, Jie Li2, Changgang Sun3,4. 1. College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China. 2. College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China. 3. Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China. 4. Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China.
Abstract
Background: Glycolysis is critical in the occurrence and development of tumors. Owing to the biological and clinical heterogeneity of patients with BRCA, the traditional predictive classification system is far from satisfactory. Survival and prognosis biomarkers related to glycolysis have broad application prospects for assessing the risk of patients and guiding their individualized treatment. Methods: The mRNA expression profiles and clinical information of patients with BRCA were obtained from TCGA database, and glycolysis-related genes were obtained by GSEA. Patients with BRCA were randomly divided into the training cohort and testing cohort. Univariate and multivariate Cox analyses were used to establish and validate a new mRNA signature for predicting the prognosis of patients with BRCA. Results: We established a four-gene breast cancer prediction signature that included PGK1, SDHC, PFKL, and NUP43. The patients with BRCA in the training cohort and testing cohort were divided into high-risk and low-risk groups based on the signature. The AUC values were 0.74 (training cohort), 0.806 (testing cohort) and 0.769 (entire cohort), thereby showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that four-gene signature could independently predict the prognosis of BRCA patients without being affected by clinical factors. Conclusion: We constructed a four-gene signature to predict the prognosis of patients with BRCA. This signature will aid in the early diagnosis and personalized treatment of breast cancer, but the specific associated biological mechanism requires further study.
Background: Glycolysis is critical in the occurrence and development of tumors. Owing to the biological and clinical heterogeneity of patients with BRCA, the traditional predictive classification system is far from satisfactory. Survival and prognosis biomarkers related to glycolysis have broad application prospects for assessing the risk of patients and guiding their individualized treatment. Methods: The mRNA expression profiles and clinical information of patients with BRCA were obtained from TCGA database, and glycolysis-related genes were obtained by GSEA. Patients with BRCA were randomly divided into the training cohort and testing cohort. Univariate and multivariate Cox analyses were used to establish and validate a new mRNA signature for predicting the prognosis of patients with BRCA. Results: We established a four-gene breast cancer prediction signature that included PGK1, SDHC, PFKL, and NUP43. The patients with BRCA in the training cohort and testing cohort were divided into high-risk and low-risk groups based on the signature. The AUC values were 0.74 (training cohort), 0.806 (testing cohort) and 0.769 (entire cohort), thereby showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that four-gene signature could independently predict the prognosis of BRCApatients without being affected by clinical factors. Conclusion: We constructed a four-gene signature to predict the prognosis of patients with BRCA. This signature will aid in the early diagnosis and personalized treatment of breast cancer, but the specific associated biological mechanism requires further study.
Authors: John Walsby-Tickle; Joan Gannon; Ingvild Hvinden; Chiara Bardella; Martine I Abboud; Areesha Nazeer; David Hauton; Elisabete Pires; Tom Cadoux-Hudson; Christopher J Schofield; James S O McCullagh Journal: Commun Biol Date: 2020-05-20
Authors: Cancan Zheng; Xiaomei Yu; Yiyao Liang; Yidong Zhu; Yan He; Long Liao; Dingkang Wang; Yanming Yang; Xingfeng Yin; Ang Li; Qingyu He; Bin Li Journal: Acta Pharm Sin B Date: 2021-09-11 Impact factor: 14.903