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Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals SNORA55 as a driver of prostate cancer progression.

ABSTRACT: Metastasis is the primary cause of death in prostate cancer (PCa) patients. Small nucleolar RNAs (snoRNAs) have long been considered "housekeeping" genes with no relevance for cancer biology. Emerging evidence has challenged this assumption, suggesting that snoRNA expression is frequently modulated during cancer progression. Despite this, no study has systematically addressed the prognostic and functional significance of snoRNAs in PCa. We performed RNA Sequencing on paired metastatic/non-metastatic PCa xenografts derived from clinical specimens. The clinical significance of differentially expressed snoRNAs was further investigated in two independent primary PCa cohorts (131 and 43 patients, respectively). The snoRNA demonstrating the strongest association with clinical outcome was quantified in PCa patient-derived serum samples and its functional relevance was investigated in PCa cells via gene expression profiling, pathway analysis and gene silencing. Our comparison revealed 21 differentially expressed snoRNAs in the metastatic vs. non-metastatic xenografts. Of those, 12 were represented in clinical databases and were further analyzed. SNORA55 emerged as a predictor of shorter relapse-free survival (results confirmed in two independent databases). SNORA55 was reproducibly detectable in serum samples from PCa patients. SNORA55 silencing in PCa cell lines significantly inhibited cell proliferation and migration. Pathway analysis revealed that SNORA55 expression is significantly associated with growth factor signaling and pro-inflammatory cytokine expression in PCa. Our results demonstrate that SNORA55 up-regulation predicts PCa progression and that silencing this non-coding gene affects PCa cell proliferation and metastatic potential, thus positioning it as both a novel biomarker and therapeutic target.

SUBMITTER: Crea F

PROVIDER: S-EPMC5423162 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals SNORA55 as a driver of prostate cancer progression.

Crea Francesco F Quagliata Luca L Michael Agnieszka A Liu Hui Hsuan HH Frumento Paolo P Azad Arun A AA Xue Hui H Pikor Larissa L Watahiki Akira A Morant Rudolf R Eppenberger-Castori Serenella S Wang Yuwei Y Parolia Abhijit A Lennox Kim A KA Lam Wan L WL Gleave Martin M Chi Kim N KN Pandha Hardev H Wang Yuzhuo Y Helgason Cheryl D CD

Molecular oncology 20151222 5

Metastasis is the primary cause of death in prostate cancer (PCa) patients. Small nucleolar RNAs (snoRNAs) have long been considered "housekeeping" genes with no relevance for cancer biology. Emerging evidence has challenged this assumption, suggesting that snoRNA expression is frequently modulated during cancer progression. Despite this, no study has systematically addressed the prognostic and functional significance of snoRNAs in PCa. We performed RNA Sequencing on paired metastatic/non-metast ...[more]

PMID: 26809501

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Project description:BackgroundEven though emerging studies supplied evidence that Adhesion Molecule with Ig Like Domain family 2 (AMIGO2) plays a critical role in numerous cancers, comprehensive analysis of the prognostic value and significant role of AMIGO2 in prostate cancer (PCa) have not been described.MethodsDifferentially expressed analysis, survival analysis and univariate cox regression analysis were first performed to explore the diagnostic and prognostic role of AMIGO2 in various cancers, especially in PCa. Tissue microarray were used to examined the association between AMGIO2 and clinical features. Multivariate cox regression analysis, concordance index, nomogram construction, the receiver operator characteristic curve and calibration curves were further used to discover the effects of AMIGO2 on recurrence-free survival (RFS) and clinicopathological characteristics, including age, Gleason score (GS) and tumor stage. Genetic and Epigenetic Alterations analysis were further conducted to explore the potential effect of AMIGO2 in PCa and examined by biological function analysis and in vitro experiments.ResultsAMIGO2 was associated with poor RFS (P<0.05) and differentially expressed (P<0.05) in multiple cancer type, especially in PCa. Besides, decreasing the expression of AMIGO2 inhibited PCa cell proliferation and colony formation in vitro. In addition, AMIGO2 was a reliable prognostic marker providing additional information (C-index: 0.7) that supplement the currently used prognosis evaluation system, e.g., T stage (C-index: 0.62) and GS (C-index: 0.65). A novel nomogram was established based on AMIGO2, tumor stage and GS with accuracies (areas under curve) of 0.70, 0.78 and 0.82 for predicting 3-, 5- and 7-year RFS, respectively. Bioinformatic analysis and in vitro examination also suggested that AMIGO2 might involve in the progression of PCa tumors inducing epithelial mesenchymal transition (EMT).ConclusionsWe identified AMIGO2 as a pan-cancer gene that could not only be a prognostic biomarker in various cancers, especially in PCa, but may functionally promoting PCa progression via EMT and mediating docetaxel resistance, suggesting AMIGO2 as a potential target for future treatment of PCa.

Project description:BackgroundStarch biosynthesis in endosperm is a key process influencing grain yield and quality in maize. Although a number of starch biosynthetic genes have been well characterized, the mechanisms by which the expression of these genes is regulated, especially in regard to microRNAs (miRNAs), remain largely unclear.ResultsSequence data for small RNAs, degradome, and transcriptome of maize endosperm at 15 and 25 d after pollination (DAP) from inbred lines Mo17 and Ji419, which exhibit distinct starch content and starch granule structure, revealed the mediation of starch biosynthetic pathways by miRNAs. Transcriptome analysis of these two lines indicated that 33 of 40 starch biosynthetic genes were differentially expressed, of which 12 were up-regulated in Ji419 at 15 DAP, one was up-regulated in Ji419 at 25 DAP, 14 were up-regulated in Ji419 at both 15 and 25 DAP, one was down-regulated in Ji419 at 15 DAP, two were down-regulated in Ji419 at 25 DAP, and three were up-regulated in Ji419 at 15 DAP and down-regulated in Ji419 at 25 DAP, compared with Mo17. Through combined analyses of small RNA and degradome sequences, 22 differentially expressed miRNAs were identified, including 14 known and eight previously unknown miRNAs that could target 35 genes. Furthermore, a complex co-expression regulatory network was constructed, in which 19 miRNAs could modulate starch biosynthesis in endosperm by tuning the expression of 19 target genes. Moreover, the potential operation of four miRNA-mediated pathways involving transcription factors, miR169a-NF-YA1-GBSSI/SSIIIa and miR169o-GATA9-SSIIIa/SBEIIb, was validated via analyses of expression pattern, transient transformation assays, and transactivation assays.ConclusionOur results suggest that miRNAs play a critical role in starch biosynthesis in endosperm, and that miRNA-mediated networks could modulate starch biosynthesis in this tissue. These results have provided important insights into the molecular mechanism of starch biosynthesis in developing maize endosperm.

Project description:BackgroundProstate cancer is one of the most common complex diseases with high leading cause of death in men. Identifications of prostate cancer associated genes and biomarkers are thus essential as they can gain insights into the mechanisms underlying disease progression and advancing for early diagnosis and developing effective therapies.MethodsIn this study, we presented an integrative analysis of gene expression profiling and protein interaction network at a systematic level to reveal candidate disease-associated genes and biomarkers for prostate cancer progression. At first, we reconstructed the human prostate cancer protein-protein interaction network (HPC-PPIN) and the network was then integrated with the prostate cancer gene expression data to identify modules related to different phases in prostate cancer. At last, the candidate module biomarkers were validated by its predictive ability of prostate cancer progression.ResultsDifferent phases-specific modules were identified for prostate cancer. Among these modules, transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were shown to be the pathway targets for prostate cancer progression. The identified candidate disease-associated genes showed better predictive ability of prostate cancer progression than those of published biomarkers. In context of functional enrichment analysis, interestingly candidate disease-associated genes were enriched in the nucleus and different functions were encoded for potential transcription factors, for examples key players as AR, Myc, ESR1 and hidden player as Sp1 which was considered as a potential novel biomarker for prostate cancer.ConclusionsThe successful results on prostate cancer samples demonstrated that the integrative analysis is powerful and useful approach to detect candidate disease-associate genes and modules which can be used as the potential biomarkers for prostate cancer progression. The data, tools and supplementary files for this integrative analysis are deposited at http://www.ibio-cn.org/HPC-PPIN/.

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Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals SNORA55 as a driver of prostate cancer progression.

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Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals SNORA55 as a driver of prostate cancer progression.

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