Evidence map›Paper›PMID 28984201›Full record

ArticleBMC genomics2017

Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets.

Vladimir A Kuznetsov, Zhiqun Tang, Anna V Ivshina

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.8field-weighted citation impact, top 31% 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

13 citing papers in PubMed, 22 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Vladimir A KuznetsovGenome and Gene Expression Data Analysis Division, Bioinformatics Institute, A-STAR, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore. vladimirk@bii.a-star.edu.sg.
Zhiqun TangGenome and Gene Expression Data Analysis Division, Bioinformatics Institute, A-STAR, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore.
Anna V IvshinaGenome and Gene Expression Data Analysis Division, Bioinformatics Institute, A-STAR, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore.
Bioinformatics Institute · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-grade serous ovarian carcinoma (HG-SOC) is the dominant tumor histologic type in epithelial ovarian cancers, exhibiting highly aberrant microRNA expression profiles and diverse pathways that collectively determine the disease aggressiveness and clinical outcomes. However, the functional relationships between microRNAs, the common pathways controlled by the microRNAs and their prognostic and therapeutic significance remain poorly understood.

methodsWe investigated the gene expression patterns of microRNAs in the tumors of 582 HG-SOC patients to identify prognosis signatures and pathways controlled by tumor miRNAs. We developed a variable selection and prognostic method, which performs a robust selection of small-sized subsets of the predictive features (e.g., expressed microRNAs) that collectively serves as the biomarkers of cancer risk and progression stratification system, interconnecting these features with common cancer-related pathways.

resultsAcross different cohorts, our meta-analysis revealed two robust and unbiased miRNA-based prognostic classifiers. Each classifier reproducibly discriminates HG-SOC patients into high-confidence low-, intermediate- or high-prognostic risk subgroups with essentially different 5-year overall survival rates of 51.6-85%, 20-38.1%, and 0-10%, respectively. Significant correlations of the risk subgroup's stratification with chemotherapy treatment response were observed. We predicted specific target genes involved in nine cancer-related and two oocyte maturation pathways (neurotrophin and progesterone-mediated oocyte maturation), where each gene can be controlled by more than one miRNA species of the distinct miRNA HG-SOC prognostic classifiers.

conclusionsWe identified robust and reproducible miRNA-based prognostic subsets of the of HG-SOC classifiers. The miRNAs of these classifiers could control nine oncogenic and two developmental pathways, highlighting common underlying pathologic mechanisms and perspective targets for the further development of a personalized prognosis assay(s) and the development of miRNA-interconnected pathway-centric and multi-agent therapeutic intervention.

Indexed as

Gene Expression ProfilingAdultCarcinogenesisFemaleGenetic Predisposition to DiseaseHumansMicroRNAsMiddle AgedOvarian NeoplasmsPrognosisMicroRNAsEMTmeta-analysismicroRNAneurotrophin signalingoncogenic pathwayovarian cancerprogesterone-mediated oocyte maturationprognostic biomarkerprognostic signaturesweighted voting grouping

Identifiers

PMID28984201
PMCPMC5629558
OpenAlexW2761649622

What OpenQuestion holds

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LicenceCC BY
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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.