five

Circulating RNAs profile in non-small cell lung cancer patients

收藏
NIAID Data Ecosystem2026-03-10 收录
下载链接:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69732
下载链接
链接失效反馈
官方服务:
资源简介:
Circulating RNAs are a less invasive and easy accessed source of samples for biomedical research and clinical applications. However, circulating mRNA is mostly fragmented and less abundant. High throughput RNA sequencing (RSEQ) and DASL assay have been both applied to profile such fragmented RNA samples. In this study, we compared the ability of transcriptomic profiling of the two platforms. Circulating RNAs from three non-small cell lung cancer patients and three age-matched healthy controls were analyzed by RSEQ and DASL assay. The concordance of each gene analyzed by the two platforms were measured with Pearson's correlation coefficient. And gene expression level determined by both platforms were confirmed by RT-PCR. The two platforms showed modest to moderate correlation. Genes with higher expression level represented higher cross-platform concordance. Compare the results of RT-PCR and the two platforms, RSEQ was much higher correlated with RT-PCR. This data suggested that RSEQ could be more suitable for circulating RNA profiling. In conclusion, we have demonstrated that genes with higher expression levels showed cross-platform concordance. And, the RSEQ could be more suitable tool for profiling circulating RNAs. Whole blood samples were obtained from three NSCLC patients and three healthy volunteers. The plasma section were collected after 1,600 x g centrifugation. The plasma RNAs were extracted with TRIzol-LS reagents. Total cell-free RNAs were quantitized with NanoDrop-1000. The gene expression levels were determined by Illumina DASL-HT12-v4 array. Gene expression determined by Illumina data analysis pipeline with quantile normalization. The pathways activated in each groups were identified with gene set enrichment analysis.
创建时间:
2017-12-22
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作