Evidence map›Paper›PMID 30013637›Full record

ArticleOncology letters2018

Identification of potential target genes in pancreatic ductal adenocarcinoma by bioinformatics analysis.

Yuchen Tang, Zixiang Zhang, Yaocheng Tang, Xinyu Chen, Jian Zhou

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
1.9field-weighted citation impact, top 11% 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

24 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
  3. Recent advances in drug delivery and targeting for the treatment of pancreatic cancer.Journal of controlled release : official journal of the Controlled Release Society · 2024
    Review
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  20. Identification for Exploring Underlying Pathogenesis and Therapy Strategy of Oral Squamous Cell Carcinoma by Bioinformatics Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2019
    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

5 authors at 1 institution in 1 country.

Yuchen TangDepartment of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Zixiang ZhangDepartment of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Yaocheng TangDepartment of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Xinyu ChenDepartment of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Jian ZhouDepartment of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most complicated and fatally pathogenic human malignancies. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of PDAC. The aim of the present study was to identify the key genes and signaling pathways associated with PDAC using bioinformatics analysis. Four transcriptome microarray datasets (GSE15471, GSE55643, GSE62165 and GSE91035) were acquired from Gene Expression Omnibus datasets, which included 226 PDAC samples and 65 normal pancreatic tissue samples. We screened differentially expressed genes (DEGs) with GEO2R and investigated their biological function by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. The overall survival data was obtained from UALCAN, which calculated the data shared with The Cancer Genome Atlas. In addition, a protein-protein interaction (PPI) network of the DEGs was constructed by STRING and Cytoscape software. The four sets of DEGs exhibited an intersection consisting of 205 genes (142 up-regulated and 63 down-regulated), which may be associated with PDAC. GO analysis showed that the 205 DEGs were significantly enriched in the plasma membrane, cell adhesion molecule activity and the Energy pathways, and glycine, serine, threonine metabolism were the most enriched pathways according to KEGG pathway analysis. Kaplan-Meier survival analysis revealed that 22 of 205 common genes were significantly associated with the overall survival of pancreatic cancer patients. In the PPI network and sub-network, DKK1 and HMGA2 were considered as hub genes with high connectivity degrees. DKK1 and HMGA2 are strongly associated with WNT3A and TP53 separately, which indicates that they may play an important role in the Wnt and P53 signaling pathways. Using integrated bioinformatics analysis, we identified DKK1 and HMGA2 as candidate genes in PDAC, which may improve our understanding of the mechanisms of the pathogenesis and integration; the two genes may be therapeutic targets and prognostic markers for PDAC.

Indexed as

bioinformatics analysisdifferentially expressed genesDKK1HMGA2PDAC

Identifiers

PMID30013637
PMCPMC6036577
OpenAlexW2805695913

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.