Preliminary construction of a regulatory network of miRNAs in the pathogenesis of nucleus pulposus degeneration - a review based on data mining.

Yingjun Guo, Xiaofei Wang, Hao Liu, Beiyu Wang, Yang Meng, Chen Ding

Open access · greenAbstract readReview

In one paragraph

Review in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.2field-weighted citation impact, top 49% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

Noncoding RNAs in skeletal development and disorders.Biological research · 2024
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

6 authors at 2 institutions in 1 country.

Yingjun GuoDepartment of Orthopedic Surgery, West China Hospital, Sichuan University Chengdu 610041, Sichuan, China.

Xiaofei WangDepartment of Orthopedic Surgery, West China Hospital, Sichuan University Chengdu 610041, Sichuan, China.

Hao LiuDepartment of Orthopedic Surgery, West China Hospital, Sichuan University Chengdu 610041, Sichuan, China.

Beiyu WangDepartment of Orthopedic Surgery, West China Hospital, Sichuan University Chengdu 610041, Sichuan, China.

Yang MengDepartment of Orthopedic Surgery, West China Hospital, Sichuan University Chengdu 610041, Sichuan, China.

Chen DingDepartment of Orthopedic Surgery, West China Hospital, Sichuan University Chengdu 610041, Sichuan, China.

Sichuan University · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we attempted to further collate existing transcriptome sequencing (mRNA-Seq) data by applying data mining and screening intervertebral disc degeneration (IVDD)-related miRNAs. At the same time, combined with published articles, the miRNAs that have been screened out were further excluded, and only the miRNAs confirmed by the reported studies were retained and reviewed. We obtained 12 pro-IVDD miRNAs and ten anti-IVDD miRNAs using the above screening process, involving 33 literature sources. By reviewing and summarizing the above studies, we preliminarily constructed the regulatory network of miRNA in the pathogenesis of IVDD. This regulatory network comprises many gaps and potential miRNA interactions, and these points may be the breakthrough points for further IVDD-related research. This new review approach can also provide a reference for the mechanistic studies of other diseases.

Indexed as

data miningIntervertebral disc degenerationmicroRNAnucleus pulposus

Identifiers

PMID34650673

PMCPMC8507033

OpenAlexW3205659710

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.