A high-throughput method to detect RNA profiling by integration of RT-MLPA with next generation sequencing technology
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Jing Wang1,*, Xue Yang1,*, Haofeng Chen2, Xuewei Wang1, Xiangyu Wang1, Yi Fang1, Zhenyu Jia3 and Jidong Gao1
1Department of Breast Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
2Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
3Department of Botany and Plant Sciences, University of California, Riverside, California, United States
*These authors have contributed equally to this work
Jidong Gao, email: [email protected]
Yi Fang, email: [email protected]
Zhenyu Jia, email: [email protected]
Keywords: breast cancer, next generation sequencing, RNA expression, MLPA, 21-gene
Received: January 17, 2017 Accepted: March 28, 2017 Published: May 02, 2017
RNA in formalin-fixed and paraffin-embedded (FFPE) tissues provides large amount of information indicating disease stages, histological tumor types and grades, as well as clinical outcomes. However, Detection of RNA expression levels in formalin-fixed and paraffin-embedded samples is extremely difficult due to poor RNA quality. Here we developed a high-throughput method, Reverse Transcription-Multiple Ligation-dependent Probe Sequencing (RT-MLPSeq), to determine expression levels of multiple transcripts in FFPE samples. By combining Reverse Transcription-Multiple Ligation-dependent Amplification method and next generation sequencing technology, RT-MLPSeq overcomes the limit of probe length in multiplex ligation-dependent probe amplification assay and thus could detect expression levels of transcripts without quantitative limitations. We proved that different RT-MLPSeq probes targeting on the same transcripts have highly consistent results and the starting RNA/cDNA input could be as little as 1 ng. RT-MLPSeq also presented consistent relative RNA levels of selected 13 genes with reverse transcription quantitative PCR. Finally, we demonstrated the application of the new RT-MLPSeq method by measuring the mRNA expression levels of 21 genes which can be used for accurate calculation of the breast cancer recurrence score – an index that has been widely used for managing breast cancer patients.
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