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Cited by 69

The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.

MiR-139-5p: promising biomarker for cancer

Zhang, He-da, Jiang, Lin-hong, Sun, Da-wei, Li, Jian, Tang, Jin-hai·2015·Tumor Biology
DOI 10.1007/s13277-015-3199-3
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Abstract

MicroRNAs (miRNAs) were reported to be associated with cancer progression and carcinogenesis. MiRNAs are small, highly conserved, small non-coding RNA molecules, consisting of 18-25 nucleotides that control gene expression at the post-transcription level. By binding to complementary binding sites within the 3' untranslated region (3'UTR) of target messenger RNAs (mRNAs), inhibiting translation or promoting degradation of mRNAs. MicroRNAs not only play an important part in regulating gene expression but also controlling diverse physiological and pathological processes. Similarly, several studies have demonstrated that miRNAs have been involved in regulating various biological processes, including apoptosis, proliferation, cellular differentiation, metabolism, signal transduction, and carcinogenesis. MiRNA-139, which is located in 11q13.4 and has anti-oncogenic and antimetastatic activity in humans, meanwhile, was identified to be downregulated in previous studies. However, based on the pathogenetic relationship between cancer and the role of miR-139-5p in tumorigenesis, we consider that miR-139-5p may be the candidates to serve as promising biomarkers with sufficient sensitivity and specificity for the diagnosis of cancer in a clinical setting; moreover, it would offer a new safe and effective way in further molecular targeting cancer treatment, as well as improving overall survival of patients.

Overview

Integrity signals

Registry

Crossref · 10.1007/s13277-015-3199-3

Cites

103 of 103 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 2

  1. Sandy L, Wong CC, Lee JM, Fan DN, Tsang FH, Ng IO, et al. Enhancer of zeste homolog 2 epigenetically silences multiple tumor suppressor microRNAs to promote liver cancer metastasis. Hepatology. 2012;56(2):622–31. · DOI
  2. Krishnan K, Steptoe AL, Martin HC, Pattabiraman DR, Nones K, Waddell N, et al. miR-139-5p is a regulator of metastatic pathways in breast cancer. RNA. 2013;19(12):1767–80. · DOI · Paper page

Cited by

See all 69

Metadata

Authors
Zhang, H.-D., Jiang, L.-H., Sun, D.-W., Li, J., & Tang, J.-H.
Venue
Tumor Biology
Publisher
Springer Science and Business Media LLC
Published
2015-02-19
Volume
36
Issue
3
Pages
1355-1365
ISSN
  • 1010-4283
  • 1423-0380
DOI
10.1007/s13277-015-3199-3
Cited by
69
References
103
Language
en
Licence
www.springer.com/tdm (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.