Literature DB >> 25975687

[Expression of colony-stimulating factor 1 in lung adenocarcinoma and its prognostic implication].

Baoxiang Pei1, Bingsheng Sun1, Yu Zhang1, Anlei Wang1, Zhenfa Zhang2.   

Abstract

OBJECTIVE: This study aimed to explore the expression of tumor-derived colony-stimulating factor 1 (CSF1), its prognostic significance and underlying related mechanisms in resected lung adenocarcinoma (ADC).
METHODS: Immunohistochemistry and tissue microarray were used to detect the expression of CSF1, epidermal growth factor receptor (EGFR), and CD68 in 266 patients with lung adenocarcinoma treated in our department between 2004 and 2008.
RESULTS: In the 266 ADC cases, the positive rates of expression of CSF1, EGFR and CD68 proteins were 56.4%, 42.1% and 81.2%, respectively. The expression level of CSF1 was positively correlated with TNM stage, number of involved nodal stations, tumor recurrence and EGFR expression (P<0.05). Univariate analysis indicated that TNM stage, number of involved lymph nodes, number of involved nodal stations, CSF1 expression, the combination of CSF1/EGFR and co-expression of CSF1/CD68/EGFR were statistically significant for prognosis (P<0.05). The results of multivariate analysis showed that TNM stage, co-expression of CSF1/EGFR and CSF1/CD68/EGFR were significant and independent risk factors for survival (P<0.05). Correlational analysis showed that expression of CSF1 and EGFR in the tumors was positively correlated to the degree of infiltration of interstitial tumor-associated macrophages (TAMs) (respectively; P<0.05).
CONCLUSIONS: The expression of CSF1 indicates a poor prognosis in postoperative lung adenocarcinoma. Co-expression of CSF1 and EGFR may be a valuable independent prognostic predictor, and its mechanism is probably involved in the interaction of cancer cells and TAMs in the progression of lung adenocarcinoma.

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Year:  2015        PMID: 25975687

Source DB:  PubMed          Journal:  Zhonghua Zhong Liu Za Zhi        ISSN: 0253-3766


  1 in total

1.  SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Authors:  Hui-Yi Lin; Dung-Tsa Chen; Po-Yu Huang; Yung-Hsin Liu; Augusto Ochoa; Jovanny Zabaleta; Donald E Mercante; Zhide Fang; Thomas A Sellers; Julio M Pow-Sang; Chia-Ho Cheng; Rosalind Eeles; Doug Easton; Zsofia Kote-Jarai; Ali Amin Al Olama; Sara Benlloch; Kenneth Muir; Graham G Giles; Fredrik Wiklund; Henrik Gronberg; Christopher A Haiman; Johanna Schleutker; Børge G Nordestgaard; Ruth C Travis; Freddie Hamdy; Nora Pashayan; Kay-Tee Khaw; Janet L Stanford; William J Blot; Stephen N Thibodeau; Christiane Maier; Adam S Kibel; Cezary Cybulski; Lisa Cannon-Albright; Hermann Brenner; Radka Kaneva; Jyotsna Batra; Manuel R Teixeira; Hardev Pandha; Yong-Jie Lu; Jong Y Park
Journal:  Bioinformatics       Date:  2017-03-15       Impact factor: 6.937

  1 in total

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