A Novel IgG–IgM Autoantibody Panel Enhances Detection of Early-stage Lung Adenocarcinoma from Benign Nodules
Autoantibodies hold promise for diagnosing lung cancer. However, their effectiveness in early-stage detection needs improvement. In this study, we investigated novel IgG and IgM autoantibodies for detecting early-stage lung adenocarcinoma (Early-LUAD) by employing a multi-step approach, including Human Proteome Microarray (HuProtTM) discovery, focused microarray verification, and ELISA validation, on 1246 individuals consisting of 634 patients with Early-LUAD (stage 0-I), 280 patients with benign lung disease (BLD), and 332 normal healthy controls (NHCs). HuProtTM selected 417 IgG/IgM candidates, and focused microarray further verified 55 significantly elevated IgG/IgM autoantibodies targeting 32 tumor-associated antigens in Early-LUAD compared to BLD/NHC/BLD+NHC. A novel panel of 10 autoantibodies (ELAVL4-IgM, GDA-IgM, GIMAP4-IgM, GIMAP4-IgG, MGMT-IgM, UCHL1-IgM, DCTPP1-IgM, KCMF1-IgM, UCHL1-IgG, and WWP2-IgM) demonstrated a sensitivity of 70.5% and a specificity of 77.0% or 80.0% for distinguishing Early-LUAD from BLD or NHC in ELISA validation. Positive predictive values for distinguishing Early-LUAD from BLD with nodules ≤ 8 mm, 9-20 mm, and > 20 mm significantly increased from 47.27%, 52.00%, and 62.90% [low-dose computed tomography (LDCT) alone] to 79.17%, 71.13%, and 87.88% (10-autoantibody panel combined with LDCT), respectively. The combined risk score (CRS), based on the 10-autoantibody panel, sex, and imaging maximum diameter, effectively stratified the risk for Early-LUAD. Individuals with 10 ≤ CRS ≤ 25 and CRS > 25 indicated a higher risk of Early-LUAD compared to the reference (CRS < 10), with adjusted odds ratios of 5.28 [95% confidence interval (CI): 3.18-8.76] and 9.05 (95% CI: 5.40-15.15), respectively. This novel panel of IgG and IgM autoantibodies offers a complementary approach to LDCT in distinguishing Early-LUAD from benign nodules.
Authors
- Zhishang Zhang (ORCID: https://orcid.org/0000-0002-7876-0216)
- Xiaohong Han (ORCID: https://orcid.org/0000-0001-9190-0167)
- Rongrong Luo (ORCID: https://orcid.org/0000-0003-0702-8471)
- Haohua Zhu (ORCID: https://orcid.org/0000-0002-7914-0289)
- Ruyun Gao (ORCID: https://orcid.org/0009-0004-3683-8734)
- Xiying Li (ORCID: https://orcid.org/0000-0002-0766-3558)
- Le Tang (ORCID: https://orcid.org/0000-0002-5192-3329)
- Mengwei Yang (ORCID: https://orcid.org/0009-0000-0652-6212)
- Ning Lou (ORCID: https://orcid.org/0000-0003-0249-1192)
- Juan Cai (ORCID: https://orcid.org/0000-0001-8336-1415)
- Guangyu Fan (ORCID: https://orcid.org/0000-0003-1561-0007)
- Jiarui Yao (ORCID: https://orcid.org/0000-0002-9835-395X)
- Yuankai Shi (ORCID: https://orcid.org/0000-0002-8685-6744)
- Shasha Wang (ORCID: https://orcid.org/0000-0001-6262-0537)
- Liyuan Dai (ORCID: https://orcid.org/0009-0007-4848-3709)
- Di Wu (ORCID: https://orcid.org/0009-0009-7526-8961)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- National Cancer Center (US)
Publication Details
- Journal
- Genomics Proteomics & Bioinformatics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1093/gpbjnl/qzae085
- Citations
- 2
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Peking Union Medical College Hospital