Evidence map›Paper›PMID 36518306›Full record

ArticleFrontiers in oncology2022

A prognostic signature based on adenosine metabolism related genes for ovarian cancer.

Weifeng Liang, Chao Zhou, Jingshu Wang, Jing Zhao, Fang Liu, Guoqiang Wang, Chunwei Xu, Yuzi Zhang, Wenxian Wang, Shangli Cai and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 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
0.5field-weighted citation impact, top 38% 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, 5 citations in OpenAlex.

  1. CAR-T cells with the CD38Cell reports. Medicine · 2026
    Article
  2. Bioinformatic Approach to Identify PotentialInternational journal of molecular sciences · 2025
    Article
  3. 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

13 authors at 6 institutions in 1 country.

Weifeng LiangDepartment of Gynecology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Chao ZhouDepartment of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
Jingshu WangMedical Department, Burning Rock Biotech, Guangzhou, China.
Jing ZhaoMedical Department, Burning Rock Biotech, Guangzhou, China.
Fang LiuMedical Department, Burning Rock Biotech, Guangzhou, China.
Guoqiang WangMedical Department, Burning Rock Biotech, Guangzhou, China.
Chunwei XuInstitute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Yuzi ZhangMedical Department, Burning Rock Biotech, Guangzhou, China.
Wenxian WangDepartment of Clinical Trial, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Shangli CaiMedical Department, Burning Rock Biotech, Guangzhou, China.
Yusheng HanMedical Department, Burning Rock Biotech, Guangzhou, China.
Lei ChangDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Peihai ZhangDepartment of Gynecology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Burning Rock Biotech (China) · CNShandong University · CNChinese Academy of Sciences · CNFirst Affiliated Hospital of Zhengzhou University · CNShanghai Jiao Tong University · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ovarian cancer is one of the most common cause of cancer death in women due to its late diagnosis and susceptibility to drug resistance. Adenosine (ADO) signaling plays a key role in immune activity and tumor progression. In this study, we constructed a signature of ADO metabolism related genes expression in patients with ovarian cancer. Methods: A total of 372 ovarian cancer patients from TCGA was used as training set and 1,137 patients from six GEO datasets were as validation set. The gene expression and drug response inhibitory concentration values for ovarian cancer cell line from GDSC were used for drug sensitivity analysis. The non-negative matrix factorization algorithm and ssGSVA were used to construct the ADO score. Results: Patients with high ADO score had shorter overall survival (OS) than those with low ADO score in both training set (HR = 1.42, 95% CI, 1.06-1.88) and validation sets (pooled HR = 1.24, 95% CI = 1.02-1.51). In GSEA analysis, genes in ATP synthesis related pathways were enriched in the low ADO score group (adjusted P value = 0.02). Further, we observed that the high ADO score group had significantly higher levels of most cancer hallmark signatures (all adjusted P values < 0.01) and T cell dysfunction and exclusion signatures than the low ADO score group (all adjusted P values < 0.001). Patients with lower ADO score tended to be sensitive to common drugs including Olaparib and Paclitaxel (adjusted P values = 0.05 and 0.04, respectively). Conclusions: In conclusion, the established ADO signature could be used as a prognostic biomarker to stratify ovarian cancer patients and had the potential to guide the drug exploitation and personalized therapy selection.

Indexed as

adenosine metabolismgene expressionovarian cancerprognostic analysissignature

Identifiers

PMID36518306
PMCPMC9742553
OpenAlexW4310244877

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.