Background and purpose: Cancer of unknown primary (CUP) represents approximately 5%~10% of malignant neoplasms. For CUP patients

identification of tumor origin allows for more specific 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 encompassing 22 main tumor types. The support vector machine-recursive feature elimination algorithm was used for feature selection and classification modelling. We further optimized the RNA isolation and real-time quantitative polymerase chain reaction (RTQ-PCR) methods for candidate gene expression profiling 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 identified a list of 96-tumor specific genes

including common tumor markers

such as cadherin 1 (CDH1)

kallikrein-related 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-fixed

paraffin-embedded samples

making it suitable for rapid clinical adoption.