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Jiu-Xian Feng

Researcher at Shanghai Jiao Tong University

Publications -  7
Citations -  115

Jiu-Xian Feng is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Lung cancer & Adenocarcinoma. The author has an hindex of 6, co-authored 7 publications receiving 108 citations.

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Expression of GPC3 protein and its significance in lung squamous cell carcinoma

TL;DR: The expression of GPC3 protein in lung squamous cell carcinoma was significantly higher than that in adjacent normal tissues, and G PC3 protein expression increased with lowering degrees of tumor differentiation, and the association of initiation, development, invasion, and metastasis of disease is warranted.
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IGF-1R and Bmi-1 expressions in lung adenocarcinoma and their clinicopathologic and prognostic significance.

TL;DR: Bmi-1 may be a good biomarker to predict the prognosis of patients with completely resected lung adenocarcinoma, and the results of multivariate Cox analysis revealed that the pathological stages and Bmi- 1 expression were independent prognostic factors.
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The expression of stem cell-related indicators as a prognostic factor in human lung adenocarcinoma.

TL;DR: The purpose of the present study was to detect the presence of BASC‐like stem cell‐related indicators, such as clara cell secretory protein (CCSP), Octamer‐4 (OCT4) and Bmi‐1, and evaluate their implications in the prognosis of patients with lung adenocarcinoma.
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Prognostic impact of vascular endothelial growth factor-A and E-cadherin expression in completely resected pathologic stage I non-small cell lung cancer.

TL;DR: Gender, vascular endothelial growth factor-A and E-cadherin expression were significant predictive factors for overall survival in completely resected pathologic stage I non-small cell lung cancer.
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A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma.

TL;DR: It is found that this unity-based classification method based on principal components of SELDI spectral data completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.