Evidence map›Paper›PMID 26722320›Full record

ArticleOncology letters2015

Construction and analysis of three networks of genes and microRNAs in adenocarcinoma.

Jiahui Ning, Xiaoxin Guo, Ning Wang, Luchen Xue

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2015. 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
0.4field-weighted citation impact, top 38% 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, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Regulatory mechanisms of microRNA expression.Journal of translational medicine · 2016
    Review
  4. Review
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

4 authors at 2 institutions in 1 country.

Jiahui NingDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering of The Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Xiaoxin GuoDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering of The Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Ning WangDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering of The Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Luchen XueDepartment of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, P.R. China ; Key Laboratory of Symbol Computation and Knowledge Engineering of The Ministry of Education, Jilin University, Changchun, Jilin 130012, P.R. China.
Jilin University · CNJilin Province Science and Technology Department · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adenocarcinoma is one of the most serious diseases that threaten human health. Numerous studies have investigated adenocarcinoma and have obtained a considerable amount of data regarding genes and microRNA (miRNA) in adenocarcinoma. However, studies have only focused on one or a small number of genes and miRNAs, and the data is stored in a scattered form, making it challenging to summarize and assess the associations between the genes and miRNAs. In the present study, three networks of genes and miRNAs in adenocarcinoma were focused on. This enabled the construction of networks of elements involved in adenocarcinoma and the analysis of these networks, rather than only discussing one gene. Transcription factors (TFs), miRNAs, and target and host genes of miRNAs in adenocarcinoma, and the regulatory associations between these elements were identified in the present study. These elements and associations were then used to construct three networks, which consisted of the differentially-expressed, associated and global networks. The similarities and differences between the three networks were compared and analyzed. In total, 3 notable TFs, consisting of TP53, phosphatase and tensin homolog and SMAD4, were identified in adenocarcinoma. These TFs were able to regulate the differentially-expressed genes and the majority of the differentially-expressed miRNAs. Certain important regulatory associations were also found in adenocarcinoma, in addition to self-regulating associations between TFs and miRNAs. The upstream and downstream elements of the differentially-expressed genes and miRNAs were recorded, which revealed the regulatory associations between genes and miRNAs. The present study clearly revealed components of the pathogenesis of adenocarcinoma and the regulatory associations between the elements in adenocarcinoma. The present study may aid the investigation of gene therapy in adenocarcinoma and provides a theoretical basis for studies of gene therapy methods as a treatment for adenocarcinoma.

Indexed as

adenocarcinomahost genesmicroRNAtarget genesthree networkstranscription factors

Identifiers

PMID26722320
PMCPMC4665316
OpenAlexW2144927864

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.