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.
A Novel IgG-IgM Autoantibody Panel Enhances Detection of Early-stage Lung Adenocarcinoma from Benign Nodules.
一种新型 IgG-IgM 自身抗体组合可提高从良性结节中检测早期肺腺癌的能力
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作者:Luo Rongrong, Li Xiying, Gao Ruyun, Yang Mengwei, Cai Juan, Dai Liyuan, Lou Nin, Fan Guangyu, Zhu Haohua, Wang Shasha, Zhang Zhishang, Tang Le, Yao Jiarui, Wu Di, Shi Yuankai, Han Xiaohong
| 期刊: | Genomics Proteomics & Bioinformatics | 影响因子: | 7.900 |
| 时间: | 2025 | 起止号: | 2025 Jan 15; 22(6):qzae085 |
| doi: | 10.1093/gpbjnl/qzae085 | 靶点: | IGM、IgG、IgM |
| 研究方向: | 肿瘤 | 疾病类型: | 肺癌 |
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