Long intergenic non-coding RNA 271 is predictive of a poorer prognosis of papillary thyroid cancer
SCIENTIFIC REPORTS
Authors: Ma, Ben; Liao, Tian; Wen, Duo; Dong, Chuanpeng; Zhou, Li; Yang, Shuwen; Wang, Yu; Ji, Qinghai
Abstract
A number of long non-coding RNAs (lncRNAs) have been found to play critical roles in oncogenesis and tumor progression. We aimed to investigate whether lncRNAs could act as prognostic biomarkers for papillary thyroid cancer (PTC) that may assist us in evaluating disease status and prognosis for patients. We found 220 lncRNAs with expression alteration from the annotated 2773 lncRNAs approved by the HUGO gene nomenclature committee in The Cancer Genome Atlas (TCGA) dataset, of which FAM41C, CTBP1-AS2, LINC00271, HAR1A, LINC00310 and HAS2-AS1 were associated with recurrence. After adjusting classical clinicopathogical factors and BRAFV600E mutation, LINC00271 was found to be an independent risk factor for extrathyroidal extension, lymph node metastasis, advanced tumor stage III/IV and recurrence in multivariate analyses. Additionally, LINC00271 expression was significantly downregulated in PTCs versus adjacent normal tissues (P < 0.001). The Gene Set Enrichment Analysis (GSEA) revealed that genes associated with cell adhesion molecules, cell cycle, P53 signaling pathway and JAK/STAT signaling pathway were remarkably enriched in lower-LINC00271 versus higher-LINC00271 tumors. In conclusion, LINC00271 was identified as a possible suppressor gene in PTC in our study, and it may serve as a potential predictor of poor prognoses in PTC.
Identification of novel universal housekeeping genes by statistical analysis of microarray data
JOURNAL OF BIOCHEMISTRY AND MOLECULAR BIOLOGY
Authors: Lee, Seram; Jo, Minjoung; Lee, Jungeun; Koh, Sang Seok; Kim, Soyoun
Abstract
Housekeeping genes are widely used as internal controls in a variety of study types, including real time RT-PCR, microarrays, Northern analysis and RNase protection assays. However, even commonly used housekeeping genes may vary in stability depending on the cell type or disease being studied. Thus, it is necessary to identify additional housekeeping-type genes that show sample-independent stability. Here, we used statistical analysis to examine a large human microarray database, seeking genes that were stably expressed in various tissues, disease states and cell lines. We further selected genes that were expressed at different levels, because reference and target genes should be present in similar copy numbers to achieve reliable quantitative results. Real time RT-PCR amplification of three newly identified reference genes, CGI-119, CTBP1 and GOLGAl, alongside three well-known housekeeping genes, B2M, GAPD, and TUBB, confirmed that the newly identified genes were more stably expressed in individual samples with similar ranges. These results collectively suggest that statistical analysis of microarray data can be used to identify new candidate housekeeping genes showing consistent expression across tissues and diseases. Our analysis identified three novel candidate housekeeping genes (CGI-119, GOLGA1, and CTBP1) that could prove useful for normalization across a variety of RNA-based techniques.