Evidence map›Paper›PMID 32934713›Full record

ArticleOncology letters2020

Identification and validation of core genes for serous ovarian adenocarcinoma via bioinformatics analysis.

Ruru Zhu, Jisen Xue, Huijun Chen, Qian Zhang

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Novel mScientific reports · 2025
    Article
  2. Review
  3. Review
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  5. New prospects of cancer therapy based on pyroptosis and pyroptosis inducers.Apoptosis : an international journal on programmed cell death · 2024
    Review
  6. Article
  7. Review
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

4 authors at 1 institution in 1 country.

Ruru ZhuDepartment of Gynaecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China.
Jisen XueDepartment of Gynaecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China.
Huijun ChenDepartment of Gynaecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China.
Qian ZhangDepartment of Gynaecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, P.R. China.
Wenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer is a fatal gynaecological malignancy in women worldwide, and serous ovarian cancer (SOC) is considered the most common histological subtype of this malignancy. Thus, the present study aimed to identify the core genes for SOC via bioinformatics analysis. The GSE18520 and GSE14407 datasets were downloaded from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs) and perform gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed to identify the core genes, while The Cancer Genome Atlas (TCGA) database was used to screen for prognosis-associated DEGs. Furthermore, clinical samples were collected for further validation of kinesin family member 11 (KIF11) gene. In the GEO analysis, a total of 198 DEGs were identified, including 81 upregulated and 117 downregulated genes compared SOC to normal tissue. GSEA across the two datasets demonstrated that 16 gene sets, including those involved in the cell cycle and DNA replication, were notably associated with SOC. A PPI network of the DEGs was constructed with 130 nodes and 387 edges. Subsequently, 20 core genes involved in the same top-ranked module were filtered out by submodule analysis. Survival analysis identified three predictive genes for SOC prognosis, including KIF11, CLDN3 and FGF13. KIF11 was identified as a core and predictive gene and thus was further validated using clinical samples. The results demonstrated that KIF11 was upregulated in tumour tissues compared with adjacent normal tissues and was associated with aggressive factors, including tumour grade, TNM stage and lymph node invasion. In conclusions, the present study identified the core genes and gene sets for SOC, thus extending the understanding of SOC occurrence and progression. Furthermore, KIF11 was identified as a promising tumour-promoting gene and a potential target for the diagnosis and treatment of SOC.

Indexed as

bioinformaticscore genesserous ovarian adenocarcinoma

Identifiers

PMID32934713
PMCPMC7471683
OpenAlexW3080154932

What OpenQuestion holds

Textmetadata
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.