Using NGS Technology for Transcriptome Comparison of Normal and Tumor Tissues in Colorectal Cancer Patients
Volume 7, Issue 2, April 2019, Pages: 65-71
Received: Apr. 22, 2019;
Published: May 23, 2019
Views 26 Downloads 7
Chen Chumo, Shanghai World Foreign Lanuage Academy, Shanghai, China
Follow on us
Colorectal cancer is a kind of malignant tumor, which results from life habits and genetic factors. This research project is called "Using NGS Technology for Transcriptome Comparison of Normal and Tumor Tissues in Colorectal Cancer Patients", which belongs to basic research. Next-generation sequencing technology (NGS), also known as high-throughput sequencing technology, can comprehensively obtain whole genome and transcriptome information of cells and tissues through experimental operations including DNA or RNA extraction, purification, library construction, and bioinformatics analysis; in this way, NGS can screen the mutation and abnormal expression of tumor genes and provide relatively accurate therapeutic targets as the guidance of medication. In this study, the transcriptome of five pairs of tumor tissue samples (t: tumor) and normal tissue samples (n: normal) from five patients with colorectal cancer were compared by NGS: the total RNA from those tissues were extracted to construct mRNA library, from which cancer-related transcriptome information was obtained through NGS-based RNA-seq technology and bioinformatics analysis, which may guide clinical research and treatment. The RNA-seq data of transcriptome information from the colorectal cancer patients reflected variably expressed pattern in the comparison of CRCs/normal tissue pairs; two notable targets, COL1A1 and SPP1, were consistent with two other previous researches and matched pathways enrichment with one of them.
NGS RNA-Seq Technology, Transcriptome Comparison, Bioinformatics Analysis, Colorectal Cancer
To cite this article
Using NGS Technology for Transcriptome Comparison of Normal and Tumor Tissues in Colorectal Cancer Patients, Science Discovery.
Vol. 7, No. 2,
2019, pp. 65-71.