Evidence map›Paper›PMID 34498424›Full record

ArticleCancer medicine2021

Identification of a novel six-gene signature with potential prognostic and therapeutic value in cervical cancer.

Xinyu Qu, Zhiwen Shi, Jingjing Guo, Chenyan Guo, Junjun Qiu, Keqin Hua

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.3field-weighted citation impact, top 17% 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

16 citing papers in PubMed, 21 citations in OpenAlex.

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  8. Establishment of a CD8+ T cells-related prognostic risk model for acral melanoma based on single-cell and bulk RNA sequencing.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024
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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

6 authors at 1 institution in 1 country.

Xinyu QuDepartment of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai, China.ORCID 0000-0001-6655-6085
Zhiwen ShiDepartment of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai, China.
Jingjing GuoDepartment of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai, China.
Chenyan GuoDepartment of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai, China.ORCID 0000-0003-1203-5705
Junjun QiuDepartment of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai, China.
Keqin HuaDepartment of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai, China.
Fudan University · CN

Funding

Artificial Intelligence Innovation Project of the Shanghai Municipal Commission of Economy and Informatization 2018-RGZN-0204Clinical Research Plan of SHDC SHDC2020CR1045BClinical Research Plan of SHDC SHDC2020CR4087National Natural Science Foundation of China 81971361Natural Science Foundation of Shanghai Science and Technology 19ZR1406900Research and Innovation Project of the Shanghai Municipal Education Commission 2019-01-07-00-07-E00050Shanghai Municipal Health Commission 202040498Shanghai "Rising Stars of Medical Talent" Youth Development Program AB83030002019004
6 · The paper itself

Abstract

introductionCervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection. MATERIALS: Differentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six-gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co-expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six-gene signature.

resultsWe discovered 178 overlapping DEGs in three microarray datasets and established a six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six-gene signature was closely associated with the immune response and tumour immune microenvironment. The six-gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer.

conclusionOur study established a novel six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six-gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six-gene signature have the potential to become novel drug targets.

Indexed as

FemaleGene Expression Regulation, NeoplasticHumansMiddle AgedPrognosisTumor MicroenvironmentUterine Cervical Neoplasmscervical cancergene signatureprognosistumour immune microenvironment

Identifiers

PMID34498424
PMCPMC8495282
OpenAlexW3197597161

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.