Evidence map›Paper›PMID 32455790›Full record

ArticleCells2020

YRNAs: New Insights and Potential Novel Approach in Head and Neck Squamous Cell Carcinoma.

Kacper Guglas, Tomasz Kolenda, Maciej Stasiak, Magda Kopczyńska, Anna Teresiak, Matthew Ibbs, Renata Bliźniak, Katarzyna Lamperska

Open access · goldAbstract read
In one paragraph

Article in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.0field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
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  10. Midsize noncoding RNAs in cancers: a new division that clarifies the world of noncoding RNA or an unnecessary chaos?Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2022
    Review
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  15. The role ofContemporary oncology (Poznan, Poland) · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Kacper GuglasLaboratory of Cancer Genetics, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.
Tomasz KolendaLaboratory of Cancer Genetics, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.
Maciej StasiakChair of Medical Biotechnology, Poznan University of Medical Sciences, ul. Rokietnicka 8, 60-101 Poznan, Poland.
Magda KopczyńskaLaboratory of Cancer Genetics, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.ORCID 0000-0002-7080-750X
Anna TeresiakLaboratory of Cancer Genetics, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.
Matthew IbbsDepartment of Tumour Pathology, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.
Renata BliźniakLaboratory of Cancer Genetics, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.
Katarzyna LamperskaLaboratory of Cancer Genetics, Greater Poland Cancer Centre, Poznan, Poland, ul. Garbary 15, 61-866 Poznan, Poland.
Poznan University of Medical Sciences · PLGreater Poland Cancer Center · PLMedical University of Warsaw · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

YRNAs are a class of non-coding RNAs that are components of the Ro60 ribonucleoprotein particle and are essential for initiation of DNA replication. Ro60 ribonucleoprotein particle is a target of autoimmune antibodies in patients suffering from systemic lupus erythematosus and Sjögren's syndrome. Deregulation of YRNAs has been confirmed in many cancer types, but not in head and neck squamous cell carcinoma (HNSCC). The main aim of this study was to determine the biological role of YRNAs in HNSCC, the expression of YRNAs, and their usefulness as potential HNSCC biomarkers. Using quantitative reverse transcriptase (qRT)-PCR, the expression of YRNAs was measured in HNSCC cell lines, 20 matched cancer tissues, and 70 FFPETs (Formaline-Fixed Paraffin-Embedded Tissue) from HNSCC patients. Using TCGA (The Cancer Genome Atlas) data, an analysis of the expression levels of selected genes, and clinical-pathological parameters was performed. The expression of low and high YRNA1 expressed groups were analysed using gene set enrichment analysis (GSEA). YRNA1 and YRNA5 are significantly downregulated in HNSCC cell lines. YRNA1 was found to be significantly downregulated in patients' tumour sample. YRNAs were significantly upregulated in T4 stage. YRNA1 showed the highest sensitivity, allowing to distinguish healthy from cancer tissue. An analysis of TCGA data revealed that expression of YRNA1 was significantly altered in the human papilloma virus (HPV) infection status. Patients with medium or high expression of YRNA1 showed better survival outcomes. It was noted that genes correlated with YRNA1 were associated with various processes occurring during cancerogenesis. The GSEA analysis showed high expression enrichment in eight vital processes for cancer development. YRNA1 influence patients' survival and could be used as an HNSCC biomarker. YRNA1 seems to be a good potential biomarker for HNSCC, however, more studies must be performed and these observations should be verified using an in vitro model.

Indexed as

AgedCell Line, TumorDisease-Free SurvivalFemaleGene Expression Regulation, NeoplasticHead and Neck NeoplasmsHumansMalePapillomaviridaeRNA, UntranslatedROC CurveSquamous Cell Carcinoma of Head and NeckTreatment OutcomeRNA, UntranslatedbiomarkercancerGSEAHNSCCRNYRo60 associated YRNAsmall non-coding RNAYRNA

Identifiers

PMID32455790
PMCPMC7290662
OpenAlexW3028047515

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.