Evidence map›Paper›PMID 31724100›Full record

ArticleDigestive diseases and sciences2020

Identification of Acute Pancreatitis-Related Genes and Pathways by Integrated Bioinformatics Analysis.

Liya Fan, Xiaoliang Hui, Ying Mao, Jun Zhou

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Article in Digestive diseases and sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 15 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Liya FanDepartment of Gastroenterology, Zhejiang Hospital of Sandun District, Zhejiang, China.
Xiaoliang HuiDepartment of General Ward 1, Zhejiang Hospital of Lingyin District, Zhejiang, China.
Ying MaoDepartment of General Ward 1, Zhejiang Hospital of Lingyin District, Zhejiang, China.
Jun ZhouDepartment of General Ward 1, Zhejiang Hospital of Lingyin District, Zhejiang, China. 34608538@qq.com.
Zhejiang Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsThe present study aimed to identify the differential expressed genes that are related to acute pancreatitis.

methodsMicroarray datasets GSE109227 and GSE3644 were downloaded from the public database and analyzed to screen the genes. Afterward, integrated analysis of these genes were performed, including gene ontology and pathway enrichment analysis, protein-protein interaction network construction, expression level evaluation in human organs, relevant miRNAs and TFs prediction, and prognosis values of hub genes in pancreatic carcinoma.

resultsA total number of 93 differential expressed genes were screened from the datasets, and EGFR, CDH1, ACTB, CD44, and VCL were identified as hub DEGs. Functional enrichment analysis demonstrated that these genes were mostly enriched in biological processes such as cell adhesion, platelet aggregation, glycoprotein binding, and also involved in multiple pathways included adherent junction, proteoglycans in cancer, bacterial invasion of epithelial cells, focal adhesion, Rap1 signaling pathway, regulation of actin cytoskeleton, and pathways in cancers. The five hub genes were all expressed in human pancreas organs with various levels. Hub gene-related network investigation predicted core miRNAs including hsa-mir-16-5p and main TFs like SOX9 with close interactions with these hub genes. Survival analysis also indicated that the high expression of EGFR, CDH1, ACTB, CD44, and VCL were significantly associated with poor prognosis in pancreatic carcinoma.

conclusionsThe study suggested that hub genes EGFR, CDH1, ACTB, CD44, and VCL may play vital role in the pathogenesis of acute pancreatitis and may serve as potential biomarkers to facilitate future acute pancreatitis diagnosis and treatment.

Indexed as

Gene Regulatory NetworksOligonucleotide Array Sequence AnalysisCarcinomaDatabases, FactualDatabases, ProteinData MiningGene Expression RegulationHumansPancreatic NeoplasmsPancreatitisTranscriptomeAcute pancreatitisBioinformatics analysisDifferentially expressed genesFunctional enrichment analysisProtein–protein interaction

Identifiers

PMID31724100
OpenAlexW2988453739

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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.