Xiaodan Fu
Central South University
9 Papers
Xiaodan Fu is an academic researcher from Central South University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 3, co-authored 3 publications.
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Papers
MiR-221-3p targets ARF4 and inhibits the proliferation and migration of epithelial ovarian cancer cells.
Qihui Wu,Xiaolei Ren,Yimin Zhang,Xiaodan Fu,Yimin Li,Yulong Peng,Qing Xiao,Tong Li,Chunli Ouyang,Yixi Hu,Yu Zhang,Wenjuan Zhou,Wenguang Yan,Ke Guo,Wei Li,Yongbin Hu,Xiaojing Yang,Guang Shu,Haofan Xue,Zhangming Wei,Yonghong Luo,Gang Yin +21 more
TL;DR: The research uncovered the tumor suppressive role of miR-221-3p in EOC and directly targeted ARF4, suggesting that miR -221- 3p might be a novel potential candidate for clinical prognosis and therapeutics of EOC.
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MicroRNA-222-3p/GNAI2/AKT axis inhibits epithelial ovarian cancer cell growth and associates with good overall survival.
Xiaodan Fu,Yimin Li,Ayesha B. Alvero,Juanni Li,Qihui Wu,Qing Xiao,Yulong Peng,Yongbin Hu,Xiang Li,Wenguang Yan,Ke Guo,Wenjuan Zhou,Yong Wang,Junwen Liu,Yu Zhang,Gil Mor,Jifang Wen,Gang Yin +17 more
TL;DR: It is found that higher expression of miR-222-3p was associated with better overall survival in EOC patients, and its level was negatively correlated with tumor growth in vivo, and the characterization of a novel regulatory axis in ovarian cancer cells, miR+3p/GNAI2/AKT is described.
MicroRNA-204 inhibits proliferation, migration, invasion and epithelial-mesenchymal transition in osteosarcoma cells via targeting Sirtuin 1
TL;DR: It is suggested that miR-204 inhibits the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) of OS cells by directly targeting Sirt1.
A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC
TL;DR: A reliable prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses.
Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies
TL;DR: In this article , an immune-related risk model developed using machine learning-based integration could improve prognostic prediction and guide personalized treatment for high grade serous ovarian cancer (HGSOC) patients.