Evidence map›Paper›PMID 32420375›Full record

ArticleBioMed research international2020

Yajun Deng, Hanyun Ma, Jinyong Hao, Qiqi Xie, Ruochen Zhao

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.7field-weighted citation impact, top 8% 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

14 citing papers in PubMed, 29 citations in OpenAlex.

  1. Developing a gene expression classifier for breast cancer diagnosis.Medical & biological engineering & computing · 2025
    Article
  2. The role and therapeutic value of NUSAP1 in human cancers.Journal of translational medicine · 2025
    Review
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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 3 institutions in 2 countries.

Yajun DengLanzhou University, Lanzhou, Gansu Province 730030, China.ORCID https://orcid.org/0000-0003-0953-6525
Hanyun MaComprehensive Cancer Center, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany.
Jinyong HaoDepartment of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province 730030, China.ORCID https://orcid.org/0000-0002-8689-7634
Qiqi XieLanzhou University, Lanzhou, Gansu Province 730030, China.ORCID https://orcid.org/0000-0003-4099-5287
Ruochen ZhaoLanzhou University, Lanzhou, Gansu Province 730030, China.ORCID https://orcid.org/0000-0003-2191-7910
Lanzhou University of Technology · CNCharité - Universitätsmedizin Berlin · DELanzhou University Second Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

Indexed as

Biomarkers, TumorDatabases, Nucleic AcidMicrotubule-Associated ProteinsMinichromosome Maintenance Complex Component 2Neoplasm ProteinsPancreatic NeoplasmsDisease-Free SurvivalFemaleHumansMaleSurvival RateBiomarkers, TumorMCM2 protein, humanMicrotubule-Associated ProteinsMinichromosome Maintenance Complex Component 2Neoplasm ProteinsNUSAP1 protein, human

Identifiers

PMID32420375
PMCPMC7206867
OpenAlexW3022695073

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.