AIM: To identify potential biomarkers of primary gallbladder cancer (PGC). METHODS: Fresh PGC, cholecystitis and normal gallbladder tissue specimens collected from 10 patients, respectively, were subjected to comparative proteomic analysis. The proteomic patterns of PGC were compared with those of cholecystitis and normal gallbladder tissues using two-dimensional gel electrophoresis (2-DE). The differentially expressed proteins were then identified using a MALDI-TOF mass spectrometer (MS) and database searches. To further validate these proteins, 20 samples of PGC tissues and normal tumor-adjacent tissues were collected for Western blot, quantitative real-time PCR, and immunohistochemical staining assay. RESULTS: Seven differentially expressed protein spots were detected by 2-ED analysis by comparing the average maps of PGC, cholecystitis and normal gallbladder tissues. Six of the seven differentially expressed proteins were identified using MALDI-TOF MS, with three overexpressed and three underexpressed in PGC tissue. Protein levels of annexin A4 (ANXA4) were significantly elevated, and heat shock protein 90-beta (Hsp90β) and dynein cytoplasmic 1 heavy chain 1 (Dync1h1) were decreased in PGC tissues relative to the normal tumor-adjacent tissues as shown by Western blot analysis. However, levels of actin, aortic smooth muscle and gamma-actin were unchanged. In addition, the mRNA levels of all 5 proteins showed similar changes to those of the protein levels (P < 0.01). Further validation by immunohistochemical analysis showed the upregulated expression of ANXA4 and decreased expression of Hsp90β and Dync1h1 in the cytoplasm of PGC tissues relative to the normal tumor-adjacent tissues. CONCLUSION: Three proteins are identified as potential biomarkers of PGC using proteomic analysis. The functions of these proteins in the carcinogenesis of PGC remain to be studied.
AIM: To identify potential biomarkers of primary gallbladder cancer (PGC). METHODS: Fresh PGC, cholecystitis and normal gallbladder tissue specimens collected from 10 patients, respectively, were subjected to comparative proteomic analysis. The proteomic patterns of PGC were compared with those of cholecystitis and normal gallbladder tissues using two-dimensional gel electrophoresis (2-DE). The differentially expressed proteins were then identified using a MALDI-TOF mass spectrometer (MS) and database searches. To further validate these proteins, 20 samples of PGC tissues and normal tumor-adjacent tissues were collected for Western blot, quantitative real-time PCR, and immunohistochemical staining assay. RESULTS: Seven differentially expressed protein spots were detected by 2-ED analysis by comparing the average maps of PGC, cholecystitis and normal gallbladder tissues. Six of the seven differentially expressed proteins were identified using MALDI-TOF MS, with three overexpressed and three underexpressed in PGC tissue. Protein levels of annexin A4 (ANXA4) were significantly elevated, and heat shock protein 90-beta (Hsp90β) and dynein cytoplasmic 1 heavy chain 1 (Dync1h1) were decreased in PGC tissues relative to the normal tumor-adjacent tissues as shown by Western blot analysis. However, levels of actin, aortic smooth muscle and gamma-actin were unchanged. In addition, the mRNA levels of all 5 proteins showed similar changes to those of the protein levels (P < 0.01). Further validation by immunohistochemical analysis showed the upregulated expression of ANXA4 and decreased expression of Hsp90β and Dync1h1 in the cytoplasm of PGC tissues relative to the normal tumor-adjacent tissues. CONCLUSION: Three proteins are identified as potential biomarkers of PGC using proteomic analysis. The functions of these proteins in the carcinogenesis of PGC remain to be studied.
Entities:
Keywords:
Annexin A4; Biomarker; Dynein cytoplasmic 1 heavy chain 1; Heat shock protein 90-beta; Primary gallbladder cancer; Proteomic analysis
Authors: C Protzel; M Richter; M Poetsch; C Kakies; U Zimmermann; C Woenckhaus; K J Klebingat; O W Hakenberg; J Giebel Journal: World J Urol Date: 2010-07-03 Impact factor: 4.226
Authors: E van Wijk; E Krieger; M H Kemperman; E M R De Leenheer; P L M Huygen; C W R J Cremers; F P M Cremers; H Kremer Journal: J Med Genet Date: 2003-12 Impact factor: 6.318
Authors: Uwe Zimmermann; Stefan Balabanov; Jürgen Giebel; Steffen Teller; Heike Junker; Dieter Schmoll; Chris Protzel; Christian Scharf; Britta Kleist; Reinhard Walther Journal: Cancer Lett Date: 2004-06-08 Impact factor: 8.679
Authors: D Williams Parsons; Siân Jones; Xiaosong Zhang; Jimmy Cheng-Ho Lin; Rebecca J Leary; Philipp Angenendt; Parminder Mankoo; Hannah Carter; I-Mei Siu; Gary L Gallia; Alessandro Olivi; Roger McLendon; B Ahmed Rasheed; Stephen Keir; Tatiana Nikolskaya; Yuri Nikolsky; Dana A Busam; Hanna Tekleab; Luis A Diaz; James Hartigan; Doug R Smith; Robert L Strausberg; Suely Kazue Nagahashi Marie; Sueli Mieko Oba Shinjo; Hai Yan; Gregory J Riggins; Darell D Bigner; Rachel Karchin; Nick Papadopoulos; Giovanni Parmigiani; Bert Vogelstein; Victor E Velculescu; Kenneth W Kinzler Journal: Science Date: 2008-09-04 Impact factor: 47.728
Authors: K Kevin Pfister; Paresh R Shah; Holger Hummerich; Andreas Russ; James Cotton; Azlina Ahmad Annuar; Stephen M King; Elizabeth M C Fisher Journal: PLoS Genet Date: 2006-01 Impact factor: 5.917
Authors: Idanya Serafín-Higuera; Olga Lilia Garibay-Cerdenares; Berenice Illades-Aguiar; Eugenia Flores-Alfaro; Marco Antonio Jiménez-López; Pavel Sierra-Martínez; Luz Del Carmen Alarcón-Romero Journal: Proteome Sci Date: 2016-09-05 Impact factor: 2.480