Kui Cao
Harbin Medical University
15 Papers
4 Citations
Kui Cao is an academic researcher from Harbin Medical University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 2, co-authored 7 publications.
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Papers
Assessment of immune status of laryngeal squamous cell carcinoma can predict prognosis and guide treatment
Xueying Wang,Kui Cao,Erliang Guo,Xionghui Mao,Changming An,Lunhua Guo,Cong Zhang,Junnan Guo,Xianguang Yang,Ji Sun,Weiwei Yang,Xiaomei Li,Susheng Miao +12 more
TL;DR: A novel risk model can guide clinicians to accurately predict prognosis, identify high-risk patients, and formulate personalized treatment plans and local and systemic inflammatory markers in patients with laryngeal squamous cell carcinoma are reliable prognostic factors.
Identification of Immune-Related LncRNA Pairs for Predicting Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma.
Xueying Wang,Kui Cao,Erliang Guo,Xionghui Mao,Lunhua Guo,Cong Zhang,Junnan Guo,Gang Wang,Xianguang Yang,Ji Sun,Susheng Miao +10 more
TL;DR: In this article, the authors used univariate COX analysis and Lasso Cox regression to identify a signature consisting of 21 immune-related lncRNA pairs (IRLPs) that predicted clinical outcome and immunotherapeutic response in HNSCC.
UBE2T Contributes to the Prognosis of Esophageal Squamous Cell Carcinoma
TL;DR: In this article, the authors explored several public databases, including The Cancer Genome Atlas (TCGA), Oncomine, and gene expression Omnibus (GEO), to explore involved signaling pathways and found significantly increased UBE2T transcript levels and DNA copy numbers in ESCC tissues.
LncRNA HAR1A Suppresses the Development of Non-Small Cell Lung Cancer by Inactivating the STAT3 Pathway
TL;DR: It was found that lncRNA Highly Accelerated Region 1A (HAR1A) was down regulated in NSCLC and a 23-gene signature derived from HAR1A-related cancer cell survival genes could predict prognosis and chemotherapy response in LUAD.
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Development of immune gene pair-based signature predictive of prognosis and immunotherapy in esophageal cancer.
TL;DR: In this paper, a novel rank-based pairwise comparison algorithm was applied to select effective IRG pairs (IRGPs), followed by constructing a prognostic IRGP signature via the least absolute shrinkage and selection operator (LASSO) regression model.