Evidence map›Paper›PMID 35655917›Full record

ArticleDisease markers2022

Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer.

GenYi Qu, Hao Wang, Cheng Tang, Guang Yang, Yong Xu

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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 2 institutions in 1 country.

GenYi QuDepartment of Urology, Zhuzhou Central Hospital, Zhuzhou 412000, China.ORCID https://orcid.org/0000-0001-9661-512X
Hao WangDepartment of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China.ORCID https://orcid.org/0000-0003-0856-3257
Cheng TangDepartment of Urology, Zhuzhou Central Hospital, Zhuzhou 412000, China.ORCID https://orcid.org/0000-0002-0045-4171
Guang YangDepartment of Urology, Zhuzhou Central Hospital, Zhuzhou 412000, China.ORCID https://orcid.org/0000-0002-7951-3823
Yong XuDepartment of Urology, Zhuzhou Central Hospital, Zhuzhou 412000, China.ORCID https://orcid.org/0000-0001-6787-1618
Zhuzhou Central Hospital · CNUniversity of South China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method. Methods: We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates. Results: From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes ( Conclusions: We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.

Indexed as

Carcinoma, Renal CellEpidermal Growth FactorKidney NeoplasmsComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansEpidermal Growth Factor

Identifiers

PMID35655917
PMCPMC9155928
OpenAlexW4281393002

What OpenQuestion holds

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LicenceCC BY
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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.