Evidence map›Paper›PMID 42350789›Full record

ArticleBritish journal of cancer2026

Circulating tumor-associated autoantibody signatures for diagnosis and prognosis in small-cell lung cancer and lung adenocarcinoma.

Chaoqi Liu, Eriko Fukuda, Yoji Sagiya, Hiromi Tsuru, Yuka Okamoto, Takayuki Morisaki, Yoichiro Kamatani, Yukinori Okada, Yutaka Suzuki, Akinori Kanai and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chaoqi LiuLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Eriko FukudaCellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan.
Yoji SagiyaLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Hiromi TsuruLaboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Yuka OkamotoLaboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Takayuki MorisakiLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Yoichiro KamataniLaboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Yukinori OkadaDepartment of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-0311-8472
Yutaka SuzukiLife Science Data Research Center, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
Akinori KanaiLife Science Data Research Center, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.ORCID http://orcid.org/0000-0003-1555-6768
Naoki GoshimaMolecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.
Chizu TanikawaLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. tanikawa@edu.k.u-tokyo.ac.jp.
Koichi MatsudaLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. kmatsuda@edu.k.u-tokyo.ac.jp.ORCID http://orcid.org/0000-0001-7292-2686
BioBank Japan Project

Funding

Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP16H01566Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP17ck0106367Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP19K22525Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP19km0405215Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP19km0405215, JP17ck0106367, JP19K22525, JP25134707, JP16H01566, JP19H03881Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP223fa627011Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP22zf0127009Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP23tk0124003Ministry of Education, Culture, Sports, Science and Technology (MEXT) JP25134707
6 · The paper itself

Abstract

backgroundTumour-associated autoantibodies (TAAbs) are promising biomarkers for cancer detection, but their induction and clinical relevance in lung cancer remain unclear.

methodsSerum samples from 695 individuals were analysed for TAAb profiling by protein-array screening and two-stage ELISA validation. Diagnostic models were constructed with identified TAAbs and compared with conventional tumour markers. Potential mechanisms, clinical and prognostic features of TAAb seropositivity were analysed and its presence in prediagnostic sera was evaluated to assess the potential for early detection.

resultsSix TAAbs for small cell lung cancer (SCLC) and four for lung adenocarcinoma (LUAD) were identified, demonstrating excellent diagnostic performance (AUC > 0.8) and outperforming ProGRP and CEA. TAAb induction correlated with antigen overexpression, somatic mutations and HLA class II amino acid polymorphisms. TAAb panel seropositivity was associated with older age and advanced stage in both subtypes, and predicted poor survival in SCLC but a favourable outcome in advanced LUAD. In prediagnostic sera, the TAAb concentration increased progressively, with detectability up to 2 years before clinical diagnosis.

conclusionsDistinct TAAb panels were identified for SCLC and LUAD, serving as accurate diagnostic markers that enable early detection and as indicators of prognosis in different clinical contexts.

Indexed as

Adenocarcinoma of LungAutoantibodiesBiomarkers, TumorLung NeoplasmsSmall Cell Lung CarcinomaAdenocarcinomaAdultAgedAntigens, NeoplasmFemaleHumansMaleMiddle AgedPrognosisAntigens, NeoplasmAutoantibodiesBiomarkers, Tumor

Identifiers

PMID42350789
PMCPMC13534672

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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.