Evidence mapPaperPMID 32636682Full record

ArticleCancer management and research2020

Identification of Chemoresistance-Associated Key Genes and Pathways in High-Grade Serous Ovarian Cancer by Bioinformatics Analyses.

Yong Wu, Lingfang Xia, Qinhao Guo, Jun Zhu, Yu Deng, Xiaohua Wu

Open access · goldAbstract read
In one paragraph

Article in Cancer management and research, 2020. 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
1.4field-weighted citation impact, top 19% 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

13 citing papers in PubMed, 24 citations in OpenAlex.

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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.

Yong Wu *Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.ORCID 0000-0003-3764-3846
Lingfang Xia *Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
Qinhao Guo *Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
Jun ZhuDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
Yu DengDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
Xiaohua WuDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.ORCID 0000-0002-6889-3541
Shanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeHigh-grade serous ovarian cancer (HGSOC) is the leading cause of death among gynecological malignancies. This is mainly attributed to its high rates of chemoresistance. To date, few studies have investigated the molecular mechanisms underlying this resistance to treatment in ovarian cancer patients. In this study, we aimed to explore these molecular mechanisms using bioinformatics analysis.

methodsWe analyzed microarray data set GSE51373, which included 16 platinum-sensitive HGSOC samples and 12 platinum-resistant control samples. Differentially expressed genes (DEGs) were identified using RStudio. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DAVID, and a DEG-associated protein-protein interaction (PPI) network was constructed using STRING. Hub genes in the PPI network were identified, and the prognostic value of the top ten hub genes was evaluated. MGP, one of the hub genes, was verified by immunohistochemistry.

resultsAll samples were confirmed to be of high quality. A total of 109 DEGs were identified, and the top ten enriched GO terms and four KEGG pathways were obtained. Specifically, the PI3K-AKT signaling pathway and the Rap1 signaling pathway were identified as having significant roles in chemoresistance in HGSOC. Furthermore, based on the PPI network, KIT, FOXM1, FGF2, HIST1H4D, ZFPM2, IFIT2, CCNO, MGP, RHOBTB3, and CDC7 were identified as hub genes. Five of these hub genes could predict the prognosis of HGSOC patients. Positive immunostaining signals for MGP were observed in the chemoresistant samples.

conclusionTaken together, the findings of this study may provide novel insights into HGSOC chemoresistance and identify important therapeutic targets.

Indexed as

bioinformatics analysischemoresistancegene expression profilinghigh-grade serous ovarian cancer

Identifiers

PMID32636682
PMCPMC7335306
OpenAlexW3037233224

What Socratic holds

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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 Socratic graph.