Dataset Information


High-grade Ovarian Cancer Associated H/ACA snoRNAs Promote Cancer Cell Proliferation and Survival

ABSTRACT: Small nucleolar RNAs (snoRNAs) are an omnipresent class of non-coding RNAs involved in the modification and processing of ribosomal RNA (rRNA). There are two types of snoRNAs, box C/D, which typically guide 2’-O-methylation, and box H/ACA, which guide pseudouridylation. As snoRNAs are required for ribosome production, the increase of which is a hallmark of cancer development, their expression would be expected to increase in proliferating cancer cells. However, the contributions of H/ACA box and C/D box snoRNAs to the biology of cancer cells remain unclear. To understand the contribution of snoRNAs to cancer progression and aggressiveness we examined their abundance patterns in high-grade serous ovarian carcinomas (HGSC) and serous borderline tumors (SBT), using a newly developed snoRNA sensitive sequencing technique. Surprisingly, the results indicate that while abundance of a selected group of H/ACA snoRNAs increases, most C/D snoRNAs remain stable or decrease in HGSC compared to SBT. This suggests that induction of ribosome biogenesis and translation in HGSC does not require a general increase in snoRNAs expression but depends upon the upregulation of a handful of key H/ACA snoRNAs. This subgroup accurately discriminates between SBT and HGSC underlining their potential as biomarkers of tumor aggressiveness. Remarkably, knockdown of HGSC associated H/ACA snoRNAs, but not their host genes, inhibits cell proliferation and induces apoptosis of model ovarian cancer cell lines. Together our data indicate that specific H/ACA snoRNAs promote tumor aggressiveness through the induction of cell proliferation and resistance to apoptosis. Overall design: Ribodepleted TGIRT-Seq librairies were generated from RNA samples originating from three serous borderline tumors and three high grade serous carcinoma tissues.

INSTRUMENT(S): Illumina NextSeq 500 (Homo sapiens)

ORGANISM(S): Homo Sapiens

SUBMITTER: Laurence Faucher-Giguère  

PROVIDER: GSE181496 | GEO | 2021-12-08


Dataset's files

Action DRS
GSE181496_CPM_matrix_allsamples.csv.gz Csv
GSE181496_rawcounts_matrix_allsamples.csv.gz Csv
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