Evidence map›Paper›PMID 26628405›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2015

Screening of Target Genes and Regulatory Function of miRNAs as Prognostic Indicators for Prostate Cancer.

Zhang Xiaoli, Wei Yawei, Liu Lianna, Li Haifeng, Zhang Hui

Open access · greenAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

20 citing papers in PubMed, 38 citations in OpenAlex.

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  9. miR-182 modulates cell proliferation and invasion in prostate cancer via targeting ST6GALNAC5.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2021
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  15. Toll-Like Receptor 4 (TLR4)/Cyclooxygenase-2 (COX-2) Regulates Prostate Cancer Cell Proliferation, Migration, and Invasion by NF-κB Activation.Medical science monitor : international medical journal of experimental and clinical research · 2018
    Article
  16. Article
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  19. Prognostic Value of Protocadherin10 (PCDH10) Methylation in Serum of Prostate Cancer Patients.Medical science monitor : international medical journal of experimental and clinical research · 2016
    Article
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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

5 authors at 1 institution in 1 country.

Zhang XiaoliLife Science Research Center of Hebei North University, Zhangjiakou, Hebei, China (mainland).
Wei YaweiBasic Medical College of Hebei North University, Zhangjiakou, Hebei, China (mainland).
Liu LiannaCollege of Laboratory Medicine of Hebei North University, Zhangjiakou, Hebei, China (mainland).
Li HaifengLife Science Research Center of Hebei North University, Zhangjiakou, Hebei, China (mainland).
Zhang HuiBasic Medical College of Hebei North University, Zhangjiakou, Hebei, China (mainland).
Hebei North University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND MicroRNAs expression profiling of prostate cancer is becoming increasingly used due to its usefulness in diagnosis, staging, prognosis, and response to treatment. The aim of this study was to screen differentially expressed miRNAs in prostate cancer and analyze the functions and signal pathways of their target genes. MATERIAL AND METHODS High-throughput data of miRNAs were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 551 samples (52 normal and 499 prostate cancer cases) and 1046 miRNAs expression values were selected for further analysis. Differentially expressed miRNAs between normal and prostate cancer tissues were identified using SAMR. StarBase and TargetScan software were used to predict the miRNAs' target group and target genes, respectively. GO functional and KEGG pathway analysis was conducted on up/down-regulated expressed miRNA with DAVID. Finally, survival analysis was performed to evaluate the association of differently expressed miRNAs signature and overall survival of prostate cancer patients. RESULTS A total of 162 miRNAs were differentially expressed between normal and prostate cancer samples, including 128 up-regulated and 38 down-regulated ones; hsa-mir-153-2, hsa-mir-92a-1, and hsa-mir-182 (up-regulated); and hsa-mir-29a, hsa-mir-10a, and hsa-mir-221 (down-regulated) were identified as good biomarkers. In GO and KEGG analysis, target genes of down-regulated miRNAs were significantly enriched in positive ion combination and JAK-STAT pathway annotation, respectively; the ones with up-regulated miRNAs were significantly enriched in the function of plasma membrane and MARK signaling pathway annotation, respectively. Patients were categorized into low- or high-score groups according to their risk scores from each miRNA. The patients in the low-score group had better overall survival compared with those in high-score group. CONCLUSIONS The 6 differentially expressed miRNAs and their target genes were used to define important molecular targets that could serve as prognostic and predictive markers in the treatment of prostate cancer. Further research on the function of the target genes in the MAPK signal pathway could provide references for treatment of prostate cancer.

Indexed as

Biomarkers, TumorCase-Control StudiesDown-RegulationGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMAP Kinase Signaling SystemMicroRNAsPrincipal Component AnalysisPrognosisProstatic NeoplasmsROC CurveUp-RegulationBiomarkers, TumorMicroRNAs

Identifiers

PMID26628405
PMCPMC4671457
OpenAlexW2185265036

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.