Evidence map›Paper›PMID 33185955›Full record

ArticleCancer science2021

Using protein microarray to identify and evaluate autoantibodies to tumor-associated antigens in ovarian cancer.

Yan Ma, Xiao Wang, Cuipeng Qiu, Jiejie Qin, Keyan Wang, Guiying Sun, Di Jiang, Jitian Li, Lin Wang, Jianxiang Shi and 5 more

Open access · goldAbstract read
In one paragraph

Article in Cancer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
3.9field-weighted citation impact, top 5% 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

33 citing papers in PubMed, 53 citations in OpenAlex.

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  16. Molecular Management of High-Grade Serous Ovarian Carcinoma.International journal of molecular sciences · 2022
    Review
  17. Molecular Biomarkers for the Early Detection of Ovarian Cancer.International journal of molecular sciences · 2022
    Review
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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

15 authors at 3 institutions in 2 countries.

Yan MaDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-1148-9572
Xiao WangHenan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Cuipeng QiuDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-3956-0430
Jiejie QinDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Keyan WangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-7241-1656
Guiying SunDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Di JiangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Jitian LiLaboratory of Molecular Biology, Henan Luoyang Orthopedic Hospital & Henan Provincial Orthopedic Institute, Zhengzhou, China.
Lin WangHenan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Jianxiang ShiHenan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-4346-3895
Peng WangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-4666-9706
Hua YeDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-3657-2417
Liping DaiHenan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Bing-Hua JiangDepartment of Pathology, The University of Iowa, Iowa City, IA, USA.
Jianying ZhangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory of Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-2938-9526
Zhengzhou University · CNUniversity of Iowa · USLuoyang Orthopedic-Traumatological Hospital of Henan Province · CN

Funding

Mechanism of lung cancer resistance to tyrosine kinase inhibitor and radiation treatmentsR01CA232587 · NCI · UNIVERSITY OF IOWA · PI SIMONE, NICOLE L · 2018 to 2022
$2.9M
NOX4 Mediates Oxidative Stress in Ovarian Tumor growth and treatment ResponseR01CA193511 · NCI · UNIVERSITY OF IOWA · PI HE, JUN · 2016 to 2020
$1.7M
Major Project of Science and Technology in Henan Province: 161100311400NCI NIH HHS R01 CA193511NCI NIH HHS R01 CA232587NIH HHS R01CA193511NIH HHS R01CA232587
6 · The paper itself

Abstract

The aim of this study was to develop a noninvasive serological diagnostic approach in identifying and evaluating a panel of candidate autoantibodies to tumor-associated antigens (TAAs) based on protein microarray technology for early detection of ovarian cancer (OC). Protein microarray based on 154 proteins encoded by 138 cancer driver genes was used to screen candidate anti-TAA autoantibodies in a discovery cohort containing 17 OC and 27 normal controls (NC). Indirect enzyme-linked immunosorbent assay (ELISA) was used to detect the content of candidate anti-TAA autoantibodies in sera from 140 subjects in the training cohort. Differential anti-TAA autoantibodies were further validated in the validation cohort with 328 subjects. Subsequently, 112 sera from the patients with ovarian benign diseases with 104 OC sera and 104 NC sera together were recruited to identify the specificity of representative autoantibodies to OC among ovarian diseases. Five TAAs (GNAS, NPM1, FUBP1, p53, and KRAS) were screened out in the discovery phase, in which four of them presented higher levels in OC than controls (P < .05) in the training cohort, which was consistent with the result in the subsequent validation cohort. An optimized panel of three anti-TAA (GNAS, p53, and NPM1) autoantibodies was identified to have relatively high sensitivity (51.2%), specificity (86.0%), and accuracy (68.6%), respectively. This panel can identify 51% of OC patients with CA125 negative. This study supports our assumption that anti-TAA autoantibodies can be considered as potential diagnostic biomarkers for detection of OC; especially a panel of three anti-TAA autoantibodies could be a good tool in immunodiagnosis of OC.

Indexed as

AdultAgedAntigens, NeoplasmAutoantibodiesBiomarkers, TumorFemaleHumansMiddle AgedNucleophosminOvarian NeoplasmsProtein Array AnalysisSensitivity and SpecificityAntigens, NeoplasmAutoantibodiesBiomarkers, TumorNPM1 protein, humanNucleophosminautoantibodyearly detectionovarian cancerprotein microarraytumor-associated antigen

Identifiers

PMID33185955
PMCPMC7894002
OpenAlexW3104551134

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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.