Evidence map›Paper›PMID 36312597›Full record

ArticleJournal of healthcare engineering2022

Bioinformatics Analysis of Common Genetic and Molecular Traits and Association of Portal Hypertension with Pulmonary Hypertension.

MingYu Chen, YouPeng Chen

Open access · hybridAbstract read
In one paragraph

Article in Journal of healthcare engineering, 2022. 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
0.4field-weighted citation impact, top 39% 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. Article
  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

2 authors at 1 institution in 1 country.

MingYu ChenThe Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China.ORCID 0000-0002-3746-1666
YouPeng ChenThe Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China.ORCID 0000-0002-0460-5416
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Portal hypertension (PH) is an important cause of pulmonary arterial hypertension(PAH), but its mechanism is still unclear. We used genetic data analysis to explore the shared genes and molecular mechanisms of PH and PAH. We downloaded the PH and PAH data from the GEO database, and used the weighted gene coexpression network analysis method (WGCNA) to analyze the coexpression modules of idiopathic noncirrhotic portal hypertension (INCPH) and cirrhotic portal hypertension (CPH) and pulmonary hypertension, respectively. Enrichment analysis was performed on the common genes, and differential gene expressions (DEGs) were used for verification. The target genes of INCPH and PAH were obtained by string and cytoscape software, and the miRNAs of target genes were predicted by miRwalk, miRDB, and TargetScan and their biological functions were analyzed; finally, we used PanglaoDB to predict the expression of target genes in cells. In WGCNA, gene modules significantly related to PAH, CPH, and INCPH were identified, and enrichment function analysis showed that the common pathway of PAH and CPH were "P53 signaling pathway," "synthesis of neutral lipids"; PAH and INCPH are "terminal," "Maintenance Regulation of Granules," and "Toxin Transport." DEGs confirmed the results of WGCNA; the common miRNA functions of PAH and cirrhosis were enriched for "P53 signaling pathway," "TGF-

Indexed as

Hypertension, PortalHypertension, PulmonaryMicroRNAsComputational BiologyEndothelial CellsGTP-Binding ProteinsHumansInflammationNuclear ProteinsTumor Suppressor Protein p53GNL3 protein, humanGTP-Binding ProteinsMicroRNAsNuclear ProteinsTumor Suppressor Protein p53

Identifiers

PMID36312597
PMCPMC9613398
OpenAlexW4306950852

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.