Evidence map›Paper›PMID 33362865›Full record

ArticleFrontiers in genetics2020

MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer.

Yuejuan Liu, Yuxia Cui, Xuefeng Bai, Chenchen Feng, Meng Li, Xiaole Han, Bo Ai, Jian Zhang, Xuecang Li, Junwei Han and 7 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 55% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
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

17 authors at 1 institution in 1 country.

Yuejuan LiuSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Yuxia CuiSchool of Nursing, Harbin Medical University, Daqing, China.
Xuefeng BaiSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Chenchen FengSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Meng LiSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Xiaole HanSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Bo AiSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Jian ZhangSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Xuecang LiSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Junwei HanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jiang ZhuSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Yong JiangSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Qi PanSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Fan WangSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Mingcong XuSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Chunquan LiSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Qiuyu WangSchool of Medical Informatics, Harbin Medical University, Daqing, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients.

methodsWe describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules.

resultsWe identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets.

conclusionsOur study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.

Indexed as

genemiRNApancreatic cancerpathwaysurvival

Identifiers

PMID33362865
PMCPMC7756031
OpenAlexW3112030949

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.