Evidence map›Paper›PMID 34115421›Full record

ArticleCancer science2021

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer.

Cuipeng Qiu, Bofei Wang, Peng Wang, Xiao Wang, Yan Ma, Liping Dai, Jianxiang Shi, Keyan Wang, Guiying Sun, Hua Ye and 1 more

Abstract 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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

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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

11 authors.

Cuipeng QiuBGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-3956-0430
Bofei WangState Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.ORCID https://orcid.org/0000-0003-3651-6802
Peng WangState Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.ORCID https://orcid.org/0000-0003-4666-9706
Xiao WangBGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Yan MaState Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.
Liping DaiBGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Jianxiang ShiBGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Keyan WangBGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Guiying SunState Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.
Hua YeState Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory of Tumor Epidemiology, Zhengzhou, China.ORCID https://orcid.org/0000-0003-3657-2417
Jianying ZhangBGI College & Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-2938-9526

Funding

Major Project of Science and Technology in Henan Province 161100311400National Science and Technology Major Project of China 2018ZX10302205Zhengzhou Major Project for Collaborative Innovation 18XTZX12007
6 · The paper itself

Abstract

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

Indexed as

AdultAntigens, NeoplasmArea Under CurveAutoantibodiesBiomarkers, TumorBreast NeoplasmsCase-Control StudiesFemaleHumansMastectomyMiddle AgedProtein Array AnalysisAntigens, NeoplasmAutoantibodiesBiomarkers, Tumorbreast cancercancer driver genesprotein microarraytumor-associated antigenstumor-associated autoantibodies

Identifiers

PMID34115421
PMCPMC8353906

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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.