Dataset Information

RNA Exosome Component EXOSC4 Amplified in Multiple Cancer Types Is Required for the Cancer Cell Survival.

ABSTRACT: The RNA exosome is a multi-subunit ribonuclease complex that is evolutionally conserved and the major cellular machinery for the surveillance, processing, degradation, and turnover of diverse RNAs essential for cell viability. Here we performed integrated genomic and clinicopathological analyses of 27 RNA exosome components across 32 tumor types using The Cancer Genome Atlas PanCancer Atlas Studies' datasets. We discovered that the EXOSC4 gene, which encodes a barrel component of the RNA exosome, was amplified across multiple cancer types. We further found that EXOSC4 alteration is associated with a poor prognosis of pancreatic cancer patients. Moreover, we demonstrated that EXOSC4 is required for the survival of pancreatic cancer cells. EXOSC4 also repressed BIK expression and destabilized SESN2 mRNA by promoting its degradation. Furthermore, knockdown of BIK and SESN2 could partially rescue pancreatic cells from the reduction in cell viability caused by EXOSC4 knockdown. Our study provides evidence for EXOSC4-mediated regulation of BIK and SESN2 mRNA in the survival of pancreatic tumor cells.

SUBMITTER: Taniue K

PROVIDER: S-EPMC8745236 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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RNA Exosome Component EXOSC4 Amplified in Multiple Cancer Types Is Required for the Cancer Cell Survival.

Taniue Kenzui K Tanu Tanzina T Shimoura Yuki Y Mitsutomi Shuhei S Han Han H Kakisaka Rika R Ono Yusuke Y Tamamura Nobue N Takahashi Kenji K Wada Youichiro Y Mizukami Yusuke Y Akimitsu Nobuyoshi N

International journal of molecular sciences 20220102 1

The RNA exosome is a multi-subunit ribonuclease complex that is evolutionally conserved and the major cellular machinery for the surveillance, processing, degradation, and turnover of diverse RNAs essential for cell viability. Here we performed integrated genomic and clinicopathological analyses of 27 RNA exosome components across 32 tumor types using The Cancer Genome Atlas PanCancer Atlas Studies' datasets. We discovered that the <i>EXOSC4</i> gene, which encodes a barrel component of the RNA ...[more]

PMID: 35008922

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Project description:The exosome and its nuclear specific subunit Rrp6 form a 3'-5' exonuclease complex that regulates diverse aspects of RNA biology including 3' end processing and degradation of a variety of noncoding RNAs (ncRNAs) and unstable transcripts. Known targets of the nuclear exosome include short (<1000 bp) RNAPII transcripts such as small noncoding RNAs (snRNAs), cryptic unstable transcripts (CUTs), and some stable unannotated transcripts (SUTs) that are terminated by an Nrd1, Nab3, and Sen1 (NNS) dependent mechanism. NNS-dependent termination is coupled to RNA 3' end processing and/or degradation by the Rrp6/exosome in yeast. Recent work suggests Nrd1 is necessary for transcriptome surveillance, regulating promoter directionality and suppressing antisense transcription independently of, or prior to, Rrp6 activity. It remains unclear whether Rrp6 is directly involved in termination; however, Rrp6 has been implicated in the 3' end processing and degradation of ncRNA transcripts including CUTs. To determine the role of Rrp6 in NNS termination globally, we performed RNA sequencing (RNA-Seq) on total RNA and perform ChIP-exo analysis of RNA Polymerase II (RNAPII) localization. Deletion of RRP6 promotes hyper-elongation of multiple NNS-dependent transcripts resulting from both improperly processed 3' RNA ends and faulty transcript termination at specific target genes. The defects in RNAPII termination cause transcriptome-wide changes in mRNA expression through transcription interference and/or antisense repression, similar to previously reported effects of depleting Nrd1 from the nucleus. Elongated transcripts were identified within all classes of known NNS targets with the largest changes in transcription termination occurring at CUTs. Interestingly, the extended transcripts that we have detected in our studies show remarkable similarity to Nrd1-unterminated transcripts at many locations, suggesting that Rrp6 acts with the NNS complex globally to promote transcription termination in addition to 3' end RNA processing and/or degradation at specific targets.

Project description:Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p = 0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide significant prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of cases and observed clinical events for a deep learning task of this type, we observed wide confidence intervals for model performance, thus highlighting that future work will benefit from larger datasets assembled for the purposes for survival modeling.

Dataset Information

RNA Exosome Component EXOSC4 Amplified in Multiple Cancer Types Is Required for the Cancer Cell Survival.

Publications

RNA Exosome Component EXOSC4 Amplified in Multiple Cancer Types Is Required for the Cancer Cell Survival.

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