Evidence map›Paper›PMID 34151839›Full record

ArticleCancer biomarkers : section A of Disease markers2021

Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis.

Simei Tu, Hao Zhang, Xiaocheng Yang, Wen Wen, Kangjing Song, Xinyi Yu, Xinjian Qu

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

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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

13 citing papers in PubMed, 18 citations in OpenAlex.

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  9. Knockdown of GNL3L Alleviates the Progression of COPD Through Inhibiting the ATM/p53 Pathway.International journal of chronic obstructive pulmonary disease · 2023
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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

7 authors.

Simei Tu
Hao Zhang
Xiaocheng Yang
Wen Wen
Kangjing Song
Xinyi Yu
Xinjian Qu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer.

objectiveThe study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer.

methodsThe gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database.

resultsA total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples.

conclusionsRFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

Indexed as

Biomarkers, TumorComputational BiologyDisease-Free SurvivalEarly Detection of CancerFemaleGene ExpressionHumansPrognosisUterine Cervical NeoplasmsBiomarkers, TumorbiomarkersCervical cancergene differential expression analysishub geneweighted gene co-expression network analysis

Identifiers

PMID34151839
PMCPMC12500063
OpenAlexW3170291543

What OpenQuestion holds

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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.