Identiifcation and validation of a novel gene expression signature for diagnosing tumor tissue origin
10.19401/j.cnki.1007-3639.2016.10.001
- VernacularTitle:一种新型肿瘤组织起源分子标志物的建立与评价
- Author:
Qifeng WANG
;
Qinghua XU
;
Jinying CHEN
;
Chenhui QIAN
;
Xiaojian LIU
;
Xiang DU
- Publication Type:Journal Article
- Keywords:
Cancer of unknown primary;
Tumor tissue origin;
Gene expression proifling;
Real-time quantitative polymerase chain reaction;
Immunohistochemistry
- From:
China Oncology
2016;26(10):801-812
- CountryChina
- Language:Chinese
-
Abstract:
Background and purpose:Cancer of unknown primary (CUP) represents approximately 5%~10%of malignant neoplasms. For CUP patients, identiifcation of tumor origin allows for more speciifc therapeutic regimens and improves outcomes.Methods:By retrieving the gene expression data from ArrayExpress and Gene Expression Omnibus data repositories, we established a comprehensive gene expression database of 5 800 tumor samples encom-passing 22 main tumor types. The support vector machine-recursive feature elimination algorithm was used for feature selection and classiifcation modelling. We further optimized the RNA isolation and real-time quantitative polymerase chain reaction (RTQ-PCR) methods for candidate gene expression proifling and applied the RTQ-PCR assays to a set of formalin-fixed, paraffin-embedded tumor samples.Results:Based on the pan-cancer transcriptome database, we identiifed a list of 96-tumor speciifc genes, including common tumor markers, such as cadherin 1 (CDH1), kallikrein-re-lated peptidase 3 (KLK3), and epidermal growth factor receptor (EGFR). Furthermore, we successfully translated the microarray-based gene expression signature to the RTQ-PCR assays, which allowed an overall success rate of 88.4% (95%CI: 83.2%-92.4%) in classifying 22 different tumor types of 206 formalin-fixed, paraffin-embedded samples. Conclusion:The 96-gene RTQ-PCR assay represents a useful tool for accurately identifying tumor origins. The assay uses RTQ-PCR and routine formalin-ifxed, paraffn-embedded samples, making it suitable for rapid clinical adoption.
