Evidence map›Paper›PMID 34821436›Full record

ArticleCancer science2022

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system.

Jinyu Wu, Peng Wang, Zhuo Han, Tiandong Li, Chuncheng Yi, Cuipeng Qiu, Qian Yang, Guiying Sun, Liping Dai, Jianxiang Shi and 2 more

Open access · goldAbstract read
In one paragraph

Article in Cancer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.6field-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

18 citing papers in PubMed, 39 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

12 authors at 1 institution in 1 country.

Jinyu WuCollege of Public Health, Zhengzhou University, Zhengzhou, China.
Peng WangCollege of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-4666-9706
Zhuo HanCollege of Public Health, Zhengzhou University, Zhengzhou, China.
Tiandong LiCollege of Public Health, Zhengzhou University, Zhengzhou, China.
Chuncheng YiCollege of Public Health, Zhengzhou University, Zhengzhou, China.
Cuipeng QiuHenan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, China.
Qian YangCollege of Public Health, Zhengzhou University, Zhengzhou, China.
Guiying SunCollege of Public Health, Zhengzhou University, Zhengzhou, China.
Liping DaiHenan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, China.
Jianxiang ShiHenan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, China.
Keyan WangHenan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, China.
Hua YeCollege of Public Health, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0000-0003-3657-2417
Zhengzhou University · CN

Funding

Major Project of Science and Technology in Henan Province 161100311400National Science and Technology Major Project of China 2018ZX10302205the key research program of higher education in Henan province 22A330003Zhengzhou Major Project for Collaborative Innovation 18XTZX12007
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen-antibody system) were taken as candidate tumor-associated antigens (TAAs). Enzyme-linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha-fetoprotein (AFP)-negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early-HCC cases.

Indexed as

Adultalpha-FetoproteinsAntigens, NeoplasmAutoantibodiesBiomarkers, TumorCarcinoma, HepatocellularComputational BiologyDiagnosis, DifferentialFemaleHumansImmunologic TestsLiver CirrhosisLiver NeoplasmsMaleMiddle AgedModels, Theoreticalalpha-FetoproteinsAntigens, NeoplasmAutoantibodiesBiomarkers, Tumorautoantibodiesbioinformaticshepatocellular carcinomaimmunodiagnosispanel

Identifiers

PMID34821436
PMCPMC8819288
OpenAlexW3217510673

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.