Evidence map›Paper›PMID 33434215›Full record

ArticlePloS one2021

Identification of biomarkers and construction of a microRNA‑mRNA regulatory network for clear cell renal cell carcinoma using integrated bioinformatics analysis.

Miaoru Han, Haifeng Yan, Kang Yang, Boya Fan, Panying Liu, Hongtao Yang

Abstract read
In one paragraph

Article in PloS one, 2021. 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
–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

4 citing papers in PubMed.

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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

6 authors.

Miaoru HanDepartment of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China.
Haifeng YanDepartment of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China.
Kang YangDepartment of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China.
Boya FanDepartment of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China.
Panying LiuDepartment of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China.
Hongtao YangDepartment of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, P. R. China.ORCID 0000-0001-6515-3700

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the recent research development, the importance of microRNAs (miRNAs) in renal clear cell carcinoma (CCRCC) has become widely known. The purpose of this study is to screen out the potential biomarkers of renal clear cell carcinoma (CCRCC) by microarray analysis. The miRNA chip (GSE16441) and mRNA chip (GSE66270) related to CCRCC were downloaded from the Gene Expression Omnibus (GEO) database. After data filtering and pretreating, R platform and a series of analysis tools (funrich3.1.3, string, Cytoscape_ 3.2.1, David, etc.) were used to analyze chip data and identify the specific and highly sensitive biomarkers. Finally, by constructing the miRNA -mRNA interaction network, it was determined that five miRNAs (hsa-mir-199a-5p, hsa-mir-199b-5p, hsa-mir-532-3p and hsa-mir-429) and two key genes (ETS1 and hapln1) are significantly related to the overall survival rate of patients.

Indexed as

Gene Regulatory NetworksBiomarkers, TumorCarcinoma, Renal CellComputational BiologyDatabases, GeneticGene Expression Regulation, NeoplasticGene OntologyHumansKidney NeoplasmsMicroRNAsProtein Interaction MapsRNA, MessengerSurvival AnalysisBiomarkers, TumorMicroRNAsRNA, Messenger

Identifiers

PMID33434215
PMCPMC7802940

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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.