Evidence map›Paper›PMID 32961635›Full record

ArticleFEBS open bio2020

Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis.

Bo Shen, Kun Li, Yuting Zhang

Open access · goldAbstract read
In one paragraph

Article in FEBS open bio, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 41% 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

4 citing papers in PubMed, 11 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.

Bo ShenDepartment of Hepatobiliary Surgery, People's Hospital of Yichun City, China.
Kun LiDepartment of Hepatobiliary Surgery, People's Hospital of Yichun City, China.
Yuting ZhangDepartment of Liver Diseases, People's Hospital of Yichun City, China.ORCID 0000-0002-6880-4289
Yichun University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detection, improving outcome. MicroRNAs (miRNAs) are small endogenous noncoding RNAs of 18-22 nucleotides in length, which have a regulatory role in the expression of target proteins. Increased evidence suggests that miRNAs are abnormally expressed in a variety of cancer malignancies. Here, we combined RNA sequencing data and clinical information from The Cancer Genome Atlas Liver Hepatocellular Carcinoma database for weighted gene coexpression network analysis to identify potential miRNA prognostic biomarkers. We constructed nine coexpression modules, allowing us to identify that miR-105-5p, miR-767-5p, miR-1266-5p, miR-4746-5p, miR-500a-3p, miR-1180-3p and miR-139-5p are significantly associated with liver cancer prognosis. We found that these miRNAs exhibit significant association with prognosis of patients with liver cancer and confirmed the expression of these miRNAs in liver cancer tissues. Multivariate Cox regression analysis showed that miR-105-5p and miR-139-5p may be considered as independent factors. In summary, here we report that seven miRNAs have potential value as prognostic biomarkers of liver cancer.

Indexed as

Computational BiologyGene Regulatory NetworksAdolescentAdultAgedAged, 80 and overBiomarkers, TumorCluster AnalysisDatabases, GeneticFemaleGene Expression Regulation, NeoplasticHumansLinear ModelsLiver NeoplasmsMaleMicroRNAsBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, Messengercompetitive endogenous RNAsliver cancermicroRNAprognosisweighted gene coexpression network analysis

Identifiers

PMID32961635
PMCPMC7609804
OpenAlexW3088882988

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.