Research article
An integrative multi-platform analysis for discovering biomarkers of osteosarcoma
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* Corresponding authors: Huazong Zeng zhz@sagc.org.cn - Zhengdong Cai czd856@vip.sohu.com
- † Equal contributors
1 Department of Orthopaedics, Tenth People's Hospital, Tongji University, Shanghai 200072, PR China
2 Department of Orthopaedics, Changhai Hospital, Second Military Medical University, Shanghai 200433, PR China
3 School of Life Sciences, Fudan University, Shanghai 200433, PR China
4 Shanghai Sensichip Co Ltd, Shanghai 200433, PR China
5 International Co-operation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai 200438, PR China
6 Shanghai Municipal Center for Disease Control & Prevention, Shanghai 200336, PR China
BMC Cancer 2009, 9:150 doi:10.1186/1471-2407-9-150
Published: 16 May 2009Abstract
Background
SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (m/z) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an integrative workflow combining data sets from both SELDI-TOF-MS and gene microarray analysis.
Methods
After extracting the information for potential biomarkers from SELDI data and microarray analysis, their associations were further inferred by link-test to identify biomarkers that could likely be used for diagnosis. Immuno-blot analysis was then performed to examine whether the expression of the putative biomarkers were indeed altered in serum from patients with osteosarcoma.
Results
Six differentially expressed protein peaks with strong statistical significances were detected by SELDI-TOF-MS. Four of the proteins were up-regulated and two of them were down-regulated. Microarray analysis showed that, compared with an osteoblastic cell line, the expression of 653 genes was changed more than 2 folds in three osteosarcoma cell lines. While expression of 310 genes was increased, expression of the other 343 genes was decreased. The two sets of biomarkers candidates were combined by the link-test statistics, indicating that 13 genes were potential biomarkers for early diagnosis of osteosarcoma. Among these genes, cytochrome c1 (CYC-1) was selected for further experimental validation.
Conclusion
Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis can accelerate the identification of tumor biomarkers. The result confirmed that CYC-1 may be a promising biomarker for early diagnosis of osteosarcoma.