Evidence map›Paper›PMID 32047181›Full record

ArticleScientific reports2020

Common and Unique microRNAs in Multiple Carcinomas Regulate Similar Network of Pathways to Mediate Cancer Progression.

Divya Niveditha, Mayank Jasoria, Jayesh Narayan, Syamantak Majumder, Sudeshna Mukherjee, Rajdeep Chowdhury, Shibasish Chowdhury

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 14% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. MiRNA-93: a novel signature in human disorders and drug resistance.Cell communication and signaling : CCS · 2023
    Review
  5. Review
  6. Review
  7. miR-139-5p and miR-451a as a Diagnostic Biomarker in LUSC.Pharmacogenomics and personalized medicine · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Review
  15. Article
  16. Article
  17. Review
  18. Circulating biomarkers for early detection of hepatocellular carcinoma.Therapeutic advances in gastroenterology · 2020
    Review
  19. Review
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

7 authors at 1 institution in 1 country.

Divya NivedithaDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India.
Mayank JasoriaDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India.ORCID http://orcid.org/0000-0003-1911-1993
Jayesh NarayanDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India.ORCID http://orcid.org/0000-0002-6454-749X
Syamantak MajumderDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India.
Sudeshna MukherjeeDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India.
Rajdeep ChowdhuryDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India.
Shibasish ChowdhuryDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani Campus, Pilani, Rajasthan, India. shiba@pilani.bits-pilani.ac.in.ORCID http://orcid.org/0000-0001-9290-3802
Birla Institute of Technology and Science, Pilani · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a complex disease with a fatal outcome. Early detection of cancer, by monitoring appropriate molecular markers is very important for its therapeutic management. In this regard, the short non-coding RNA molecules, microRNAs (miRNAs) have shown great promise due to their availability in circulating fluids facilitating non-invasive detection of cancer. In this study, an in silico comparative analysis was performed to identify specific signature miRNAs dysregulated across multiple carcinomas and simultaneously identify unique miRNAs for each cancer type as well. The miRNA-seq data of cancer patient was obtained from GDC portal and their differential expressions along with the pathways regulated by both common and unique miRNAs were analyzed. Our studies show twelve miRNAs commonly dysregulated across seven different cancer types. Interestingly, four of those miRNAs (hsa-mir-210, hsa-mir-19a, hsa-mir-7 and hsa-mir-3662) are already reported as circulatory miRNAs (circRNAs); while, the miR-183 cluster along with hsa-mir-93 have been found to be incorporated in exosomes signifying the importance of the identified miRNAs for their use as prospective, non-invasive biomarkers. Further, the target mRNAs and pathways regulated by both common and unique miRNAs were analyzed, which interestingly had significant commonality. This suggests that miRNAs that are commonly de-regulated and specifically altered in multiple cancers might regulate similar pathways to promote cancer. Our data is of significance because we not only identify a set of common and unique miRNAs for multiple cancers but also highlight the pathways regulated by them, which might facilitate the development of future non-invasive biomarkers conducive for early detection of cancers.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksBiomarkers, TumorDatabases, FactualDisease ProgressionGene Expression ProfilingHumansMicroRNAsNeoplasmsBiomarkers, TumorMicroRNAs

Identifiers

PMID32047181
PMCPMC7012856
OpenAlexW3005922702

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.