Evidence map›Paper›PMID 29022480›Full record

ArticleTumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine2017

Evaluation of serum autoantibodies against tumor-associated antigens as biomarkers in lung cancer.

Pei Li, Jian-Xiang Shi, Meng-Tao Xing, Li-Ping Dai, Ji-Tian Li, Jian-Ying Zhang

Open access · goldAbstract read
PubMed Publisher
In one paragraph

Article in Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine, 2017. 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
1.5field-weighted citation impact, top 19% 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, 31 citations in OpenAlex.

  1. Antibodies as biomarkers for cancer risk: a systematic review.Clinical and experimental immunology · 2022
    Pooled it
  2. Review
  3. Article
  4. Article
  5. Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.Journal of experimental & clinical cancer research : CR · 2024
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Beneficial autoimmunity improves cancer prognosis.Nature reviews. Clinical oncology · 2021
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Pei Li1 The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
Jian-Xiang Shi1 The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
Meng-Tao Xing2 Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX, USA.
Li-Ping Dai1 The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
Ji-Tian Li2 Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX, USA.
Jian-Ying Zhang1 The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
The University of Texas at El Paso · USZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We evaluated autoantibodies against nine tumor-associated antigens, including p62, p16, Koc, p53, Cyclin B1, Cyclin E, Survivin, HCC1, and RalA as serological markers in lung cancer. Enzyme-linked immunosorbent assay (ELISA) was used to detect autoantibodies in sera from 50 lung cancer patients and 42 normal controls. Then, four tumor-associated antigens of higher values were selected and validated in sera from validation group. Western blot and serum absorption test were used to confirm positive findings from ELISA. When cutoff values were set as mean optical density values plus 3 standard deviation of normal controls, the positive rate of autoantibodies against four tumor-associated antigens (Survivin, Cyclin B1, HCC1, and p53) reached 32%, 20%, 22%, and 18%, with area under the curve values of 0.653, 0.767, 0.622, and 0.623 in sera from 50 lung cancer, respectively (all p < 0.05). Results from the validation group confirmed the results. When lung cancer patients were divided by their clinicopathological characteristics into different subgroups, we have found that serum anti-Cyclin B1 and anti-HCC1 autoantibodies increased in stages 1, 2, and 3 lung cancer; anti-Survivin autoantibodies increased in stages 2 and 3 lung cancer; and anti-p53 autoantibody only increased in stage 1 when compared with their corresponding levels in controls (all p < 0.05). Serum anti-Cyclin B1 and anti-Survivin autoantibodies increased with disease histological grade 2 and 3 (both p < 0.05). And higher serum level of anti-p53 autoantibodies is positively associated with tumor size. Parallel utilization of these four anti-tumor-associated antigens (any positive) can increase sensitivity to 65.0% at 100% specificity with area under the curve of 0.908 ( p < 0.001) in lung cancer detection in validation group. Our results suggest that autoantibodies against these four tumor-associated antigens have higher values in lung cancer detection, and serum anti-Cyclin-B1 has the potential to serve as novel non-invasive biomarkers in early-stage lung cancer.

Indexed as

AgedAged, 80 and overAntigens, NeoplasmAutoantibodiesBiomarkers, TumorCyclin B1Enzyme-Linked Immunosorbent AssayFemaleHumansInhibitor of Apoptosis ProteinsLung NeoplasmsMaleMiddle AgedNeoplasm StagingNuclear ProteinsSurvivinAntigens, NeoplasmAutoantibodiesBiomarkers, TumorBIRC5 protein, humanCyclin B1Inhibitor of Apoptosis ProteinsNuclear ProteinsSARNP protein, humanSurvivinTP53 protein, humanTumor Suppressor Protein p53autoantibodybiomarkerimmunodiagnosisLung cancertumor-associated antigen

Identifiers

PMID29022480
OpenAlexW2762161655

What OpenQuestion holds

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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.