Evidence map›Paper›PMID 30786859›Full record

SynthesisBMC cancer2019

Small RNA expression from viruses, bacteria and human miRNAs in colon cancer tissue and its association with microsatellite instability and tumor location.

Robin Mjelle, Wenche Sjursen, Liv Thommesen, Pål Sætrom, Eva Hofsli

Open access · goldAbstract readComparative StudyMeta-Analysis
In one paragraph

Synthesis in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

27 citing papers in PubMed, 36 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Novel Diagnostic Biomarkers in Colorectal Cancer.International journal of molecular sciences · 2022
    Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. 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

5 authors at 2 institutions in 1 country.

Robin MjelleDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, NTNU, Erling Skjalgssons gt 1, 7030, Trondheim, Norway. robin.mjelle@ntnu.no.
Wenche SjursenDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, NTNU, Erling Skjalgssons gt 1, 7030, Trondheim, Norway.
Liv ThommesenDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, NTNU, Erling Skjalgssons gt 1, 7030, Trondheim, Norway.
Pål SætromDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, NTNU, Erling Skjalgssons gt 1, 7030, Trondheim, Norway.
Eva HofsliDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, NTNU, Erling Skjalgssons gt 1, 7030, Trondheim, Norway.
Norwegian University of Science and Technology · NOSt Olav's University Hospital · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs (miRNA) and other small RNAs are frequently dysregulated in cancer and are promising biomarkers for colon cancer. Here we profile human, virus and bacteria small RNAs in normal and tumor tissue from early stage colon cancer and correlate the expression with clinical parameters.

methodsSmall RNAs from colon cancer tissue and adjacent normal mucosa of 48 patients were sequenced using Illumina high-throughput sequencing. Clinical parameters were correlated with the small RNA expression data using linear models. We performed a meta-analysis by comparing publicly available small RNA sequencing datasets with our original sequencing data to confirm the main findings.

resultsWe identified 331 differentially expressed miRNAs between tumor and normal samples. We found that the major changes in miRNA expression between left and right colon are due to miRNAs located within the Hox-developmental genes, including miR-10b, miR-196b and miR-615. Further, we identified new miRNAs associated with microsatellite instability (MSI), including miR-335, miR-26 and miR-625. We performed a meta-analysis on all publicly available miRNA-seq datasets and identified 117 common miRNAs that were differentially expressed between tumor and normal tissue. The miRNAs miR-135b and miR-31 were the most significant upregulated miRNA in tumor across all datasets. The miRNA miR-133a was the most strongly downregulated miRNA in our dataset and also showed consistent downregulation in the other datasets. The miRNAs associated with MSI and tumor location in our data showed similar changes in the other datasets. Finally, we show that small RNAs from Epstein-Barr virus and Fusobacterium nucleatum are differentially expressed between tumor and normal adjacent tissue.

conclusionsSmall RNA profiling in colon cancer tissue revealed novel RNAs associated with MSI and tumor location. We show that Fusobacterium nucleatum are detectable at the RNA-level in colon tissue, and that both Fusobacterium nucleatum and Epstein-Barr virus separate tumor and normal tissue.

Indexed as

Microsatellite InstabilityAdultAgedAged, 80 and overBiomarkers, TumorCohort StudiesColonic NeoplasmsDatabases, GeneticFemaleFusobacterium nucleatumGene Expression ProfilingGene Expression Regulation, NeoplasticHerpesvirus 4, HumanHigh-Throughput Nucleotide SequencingHumansMaleBiomarkers, TumorMicroRNAsMIRN155 microRNA, humanRNA, BacterialRNA, ViralColon cancerEpstein-Barr virusFusobacterium nucleatumHigh throughput sequencingisomiRmiRNAncRNAsRNA

Identifiers

PMID30786859
PMCPMC6381638
OpenAlexW2919941601

What OpenQuestion holds

Textmetadata
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.