Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Persson, Helena; Søkilde, Rolf; Häkkinen, Jari; Vallon‐Christersson, Johan; Mitelman, Felix; Borg, Åke; Höglund, Mattias; Rovira, Carlos

doi:10.1002/ijc.32927

Cited by 9 publications

(13 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We believe that SNORD63 and SNORD96A play crucial role in the tumorigenesis and development of ccRCC, but the underlying molecular mechanisms needs further studies to illuminate. As described above, snoRNAs are stably expressed and measurable in body fluids, empowering them the potential as biomarkers in various cancer types, such as NSCLC [16][17][18][19][20], CRC [21][22][23][24], breast cancer [25][26][27] and prostate cancer [28][29][30]. In current study, we demonstrated that SNORD63 and SNORD96A acted as the non-invasive diagnostic biomarkers for ccRCC even for early stage ccRCC.…”

Section: Discussionsupporting

confidence: 52%

SNORD63 and SNORD96A as the non-invasive diagnostic biomarkers for clear cell renal cell carcinoma

et al. 2021

View full text Add to dashboard Cite

Background Increasing evidence has demonstrated that snoRNAs play crucial roles in tumorigenesis of various cancer types. However, researches on snoRNAs in ccRCC were very little. This study mainly aimed to validate the differential expression and the potential diagnostic value of SNORD63 and SNORD96A in ccRCC. Methods SnoRNAs expression was downloaded from the SNORic and TCGA database including 516 patients with ccRCC and 71 control cases. SNORD63 and SNORD96A expression were further detected in 54 tumor and adjacent FFPE ccRCC tissues, 55 plasma and 75 urinary sediment of ccRCC patients. Then, differential expression and diagnostic value of SNORD63 and SNORD96A were further calculated. Results SNORD63 and SNORD96A expression were significantly increased in ccRCC tissues compared with normal tissues from the TCGA database (both, P < 0.0001). In addition, we found that SNORD63 and SNORD96A localized in plasma and US stably after treating with RNase A. Meanwhile, SNORD63 and SNORD96A in FFPE and US were elevated in ccRCC patients (all, P < 0.0001). However, plasma SNORD63 expression had no significance while SNORD96A significantly increased in plasma of ccRCC patients. Notably, the AUC of SNORD63 in US was 0.7055, by comparison the AUC of plasma SNORD63 was only 0.5161. However, the AUC of plasma SNORD96A was up to 0.8909, by comparison the AUC of SNORD96A in US was 0.6788. Interestingly, the AUC of plasma SNORD96A in early stage ccRCC was highly up to 0.9359. Conclusions Our findings revealed that SNORD63 in US and SNORD96A in plasma could act as the promising non-invasive diagnostic biomarkers for ccRCC patients.

show abstract

Section: Discussionsupporting

confidence: 52%

SNORD63 and SNORD96A as the non-invasive diagnostic biomarkers for clear cell renal cell carcinoma

et al. 2021

View full text Add to dashboard Cite

show abstract

“…3 D). As we have previously reported miRNA and snoRNA host genes to be enriched in fusion events [ 2 , 3 ], we modeled the probability of a gene being part of a validated fusion event against its status as miRNA host, snoRNA host, and gene length in a logistic regression model. Because of the large differences in expression and gene length between protein-coding and non-coding genes, the analysis was limited to genes annotated by GENCODE as protein-coding.…”

Section: Resultsmentioning

confidence: 99%

“…These can encode chimeric proteins or alter the regulation of gene expression through promoter-swapping. We have previously shown that non-coding and out-of-frame fusions can deregulate the expression of intronically encoded small non-coding RNAs including microRNA (miRNA) and small nucleolar RNA (snoRNA) [ 1 – 3 ]. There are many well-established examples of oncogenic gene fusions and some have been successfully exploited as targets for therapy.…”

Section: Introductionmentioning

confidence: 99%

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

Hafstað,

Häkkinen,

Larsson

et al. 2023

BMC Genomics

Self Cite

View full text Add to dashboard Cite

Background Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts. Results We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining. Conclusions A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

show abstract

“…Host genes of canonical snoRNAs were removed from the lists of non‐host 5′ partners as they have also been shown to be overrepresented among fusion transcripts. 27 As a background for the overrepresentation analyses we used all expressed genes in the TCGA‐BRCA cohort. All significant gene sets are included in Table S4 .…”

Section: Resultsmentioning

confidence: 99%

“…This is problematic for the statistics of overrepresentation analysis, so we created gene lists of equal length for every tumour subgroup by randomly selecting equally large sets of non‐host 5′ partners. Host genes of canonical snoRNAs were removed from the lists of non‐host 5′ partners as they have also been shown to be overrepresented among fusion transcripts 27 . As a background for the overrepresentation analyses we used all expressed genes in the TCGA‐BRCA cohort.…”

Section: Resultsmentioning

confidence: 99%

Regulatory networks and 5′ partner usage of miRNA host gene fusions in breast cancer

Hafstað

Søkilde

Häkkinen

et al. 2022

Intl Journal of Cancer

Self Cite

View full text Add to dashboard Cite

Genomic rearrangements in cancer cells can create gene fusions where the juxtaposition of two different genes leads to the production of chimeric proteins or altered gene expression through promoter-swapping. We have previously shown that fusion transcripts involving microRNA (miRNA) host genes contribute to deregulation of miRNA expression regardless of the protein-coding potential of these transcripts. Many different genes can also be used as 5 0 partners by a miRNA host gene in what we named recurrent miRNA-convergent fusions. Here, we have explored the properties of 5 0 partners in fusion transcripts that involve miRNA hosts in breast tumours from The Cancer Genome Atlas (TCGA). We hypothesised that firstly, 5 0 partner genes should belong to pathways and transcriptional programmes that reflect the tumour phenotype and secondly, there should be a selection for fusion events that shape miRNA expression to benefit the tumour cell through the known hallmarks of cancer. We found that the set of 5 0 partners in miRNA host fusions is non-random, with overrepresentation of highly expressed genes in pathways active in cancer including epithelial-to-mesenchymal transition, translational regulation and oestrogen signalling. Furthermore, many miRNAs were upregulated in samples with host gene fusions, including established oncogenic miRNAs such as mir-21 and the mir-106b~mir-93~mir-25 cluster. To the list of mechanisms for deregulation of miRNA expression, we have added fusion transcripts that change the promoter region. We propose that this adds material for genetic selection and tumour evolution in cancer cells and that miRNA host fusions can act as tumour 'drivers'.

show abstract

Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Cited by 9 publications

References 43 publications

SNORD63 and SNORD96A as the non-invasive diagnostic biomarkers for clear cell renal cell carcinoma

SNORD63 and SNORD96A as the non-invasive diagnostic biomarkers for clear cell renal cell carcinoma

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

Regulatory networks and 5′ partner usage of miRNA host gene fusions in breast cancer

Contact Info

Product

Resources

About