Evidence map›Paper›PMID 30950146›Full record

ArticleCancer science2019

Using recursive partitioning approach to select tumor-associated antigens in immunodiagnosis of gastric adenocarcinoma.

Jiejie Qin, Shuaibing Wang, Jianxiang Shi, Yan Ma, Keyan Wang, Hua Ye, Xiaojun Zhang, Peng Wang, Xiao Wang, Chunhua Song and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cancer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 15% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 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

14 authors at 2 institutions in 1 country.

Jiejie QinDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Shuaibing WangThird Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jianxiang ShiHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Yan MaDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Keyan WangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Hua YeDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Xiaojun ZhangHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Peng WangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Xiao WangHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Chunhua SongDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0001-6028-5923
Liping DaiHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Kaijuan WangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Binghua JiangHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
Jianying ZhangDepartment of Epidemiology and Health Statistics & Henan Key Laboratory for Tumor Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-2938-9526
Zhengzhou University · CNThird Affiliated Hospital of Zhengzhou University · CN

Funding

Major Project of Science and Technology in Henan Province 16110311400Program of Natural Science Foundation of Henan Province; and the Key Project of Tackling Key Problems in Science and Technology of Henan Province 182102310110Program of Natural Science Foundation of Henan Province; and the Key Project of Tackling Key Problems in Science and Technology of Henan Province 182300410009Program of Natural Science Foundation of Henan Province; and the Key Project of Tackling Key Problems in Science and Technology of Henan Province 192102310042The National Science and Technology Major Project of China 2018ZX10302205
6 · The paper itself

Abstract

The present study aimed to select anti-tumor-associated antigen (TAA) autoantibodies as biomarkers in the immunodiagnosis of gastric adenocarcinoma (GAC) by the recursive partitioning approach (RPA) and further construct and evaluate a predictive model. A case-control study was designed including 407 GAC patients as the case group and 407 normal controls. In addition, 67 serial serum samples from 25 GAC patients were collected at different time points before and after gastrectomy treatment. Autoantibodies against 14 TAA were measured in sera from all subjects by enzyme immunoassay. Finally, RPA resulted in the selection of nine-panel TAA (c-Myc, p16, HSPD1, PTEN, p53, NPM1, ENO1, p62, HCC1.4) from all detected TAA in the case-control study; the classification tree based on this nine-TAA panel had area under curve (AUC) of 0.857, sensitivity of 71.5% and specificity of 71.3%; The optimal panel also can identify GAC patients at an early stage from normal individuals, with AUC of 0.737, sensitivity of 64.9% and specificity of 70.5%. However, frequencies of the nine autoantibodies showed no correlation with GAC stage, tumor size, lymphatic metastasis or differentiation. GAC patients positive for more than two autoantibodies in the nine-TAA panel had a worse prognosis than that of the GAC patients positive for no or one antibody. Titers of 10 autoantibodies in serial serum samples were significantly higher in GAC patients after surgical resection than before. In conclusion, this study showed that the panel of nine multiple TAAs could enhance the detection of anti-TAA antibodies in GAC, and may be potential prognostic biomarkers in GAC.

Indexed as

AdenocarcinomaAdultAgedAged, 80 and overAntigens, NeoplasmAutoantibodiesBiomarkers, TumorCase-Control StudiesFemaleHumansImmunologic TestsMaleMiddle AgedMultivariate AnalysisNucleophosminROC CurveAntigens, NeoplasmAutoantibodiesBiomarkers, TumorNPM1 protein, humanNucleophosminautoantibodybiomarkergastric adenocarcinomarecursive partitioning approachtumor-associated antigen

Identifiers

PMID30950146
PMCPMC6550128
OpenAlexW2930334285

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LicenceCC BY-NC-ND
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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.