Evidence map›Paper›PMID 30384176›Full record

ArticleComputers in biology and medicine2018

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis.

Yu Hu, Hayley Dingerdissen, Samir Gupta, Robel Kahsay, Vijay Shanker, Quan Wan, Cheng Yan, Raja Mazumder

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
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

29 citing papers in PubMed.

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  13. microRNA-Based Cancer Diagnosis and Therapy.International journal of molecular sciences · 2023
    Article
  14. Review
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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.

Yu HuThe Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, 20037, USA. Electronic address: yuhu335@gwmail.gwu.edu.
Hayley DingerdissenThe Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, 20037, USA. Electronic address: hmhamilt@gwmail.gwu.edu.
Samir GuptaDepartment of Computer and Information Science, University of Delaware, Newark, DE, 19716, USA. Electronic address: sgupta@udel.edu.
Robel KahsayThe Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, 20037, USA. Electronic address: rykahsay@email.gwu.edu.
Vijay ShankerDepartment of Computer and Information Science, University of Delaware, Newark, DE, 19716, USA. Electronic address: vijay@udel.edu.
Quan WanThe Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, 20037, USA. Electronic address: wanquan@gwmail.gwu.edu.
Cheng YanThe Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, 20037, USA. Electronic address: chengyan@gwmail.gwu.edu.
Raja MazumderThe Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, 20037, USA; The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC, 20037, USA. Electronic address: mazumder@gwu.edu.

Funding

Integration of comprehensive cancer mutation and expression-associated data for biomarker evaluation and discoveryU01CA215010 · NCI · GEORGE WASHINGTON UNIVERSITY · PI CRICHTON, DANIEL, MAZUMDER, RAJA · 2017 to 2019
$1.3M
NCI NIH HHS U01 CA215010
6 · The paper itself

Abstract

microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksGenomicsHumansMicroRNAsNeoplasmsMicroRNAsDifferential expression analysisFunctional analysismiRNAmiRNA targetPan-cancer

Identifiers

PMID30384176
PMCPMC6279243

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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.