Henan Institute of Medical and Pharmaceutical Sciences & School of Basic Medical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou, China.
Henan Key Laboratory of Tumor Epidemiology & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China.
Front Immunol. 2022 Jan 24;12:728853. doi: 10.3389/fimmu.2021.728853. eCollection 2021.
Immunoglobulin M (IgM) autoantibodies, as the early appearing antibodies in humoral immunity when stimulated by antigens, might be excellent biomarkers for the early detection of lung cancer (LC). We aimed to develop a multi-analyte integrative model combining IgM autoantibodies and a traditional tumor biomarker that could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of lung adenocarcinoma (LUAD). A customized protein array based on cancer driver genes was constructed and applied in the discovery cohort consisting of 68 LUAD patients and 68 normal controls (NCs); 31 differentially expressed IgM autoantibodies were identified. The top 5 candidate IgM autoantibodies [based on the area under the receiver operating characteristic curve (AUC) ranking], namely, TSHR, ERBB2, survivin, PIK3CA, and JAK2, were validated in the validation cohort using enzyme-linked immunosorbent assay (ELISA), which included 147 LUAD samples, 72 lung squamous cell carcinoma (LUSC) samples, 44 small cell lung carcinoma (SCLC) samples, and 147 NCs. These indicators presented diagnostic capacity for LUAD, with AUCs of 0.599, 0.613, 0.579, 0.601, and 0.633, respectively ( < 0.05). However, none of them showed a significant difference between the SCLC and NC groups, and only the IgM autoantibody against JAK2 showed a higher expression in LUSC than in NC ( = 0.046). Through logistic regression analysis, with the five IgM autoantibodies and carcinoembryonic antigen (CEA), one diagnostic model was constructed for LUAD. The model yielded an AUC of 0.827 (sensitivity = 56.63%, specificity = 93.98%). The diagnostic efficiency was superior to that of either CEA (AUC = 0.692) or IgM autoantibodies alone (AUC = 0.698). Notably, the accuracy of this model in early-stage LUAD reached 83.02%. In conclusion, we discovered and identified five novel IgM indicators and developed a multi-analyte model combining IgM autoantibodies and CEA, which could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of LUAD.
免疫球蛋白 M（IgM）自身抗体是抗原刺激体液免疫时早期出现的抗体，可能是肺癌（LC）早期检测的优秀生物标志物。我们旨在开发一种多分析物综合模型，将 IgM 自身抗体与传统肿瘤标志物相结合，这可能是一种有价值且强大的辅助诊断工具，并可能提高肺腺癌（LUAD）早期检测的准确性。构建了基于癌症驱动基因的定制蛋白质阵列，并应用于由 68 例 LUAD 患者和 68 例正常对照（NC）组成的发现队列；鉴定出 31 种差异表达的 IgM 自身抗体。基于受试者工作特征曲线（ROC）下面积（AUC）排名，前 5 种候选 IgM 自身抗体[基于 AUC 排名]，即 TSHR、ERBB2、survivin、PIK3CA 和 JAK2，使用酶联免疫吸附试验（ELISA）在包含 147 例 LUAD 样本、72 例肺鳞癌（LUSC）样本、44 例小细胞肺癌（SCLC）样本和 147 例 NC 的验证队列中进行了验证。这些指标对 LUAD 具有诊断能力，AUC 分别为 0.599、0.613、0.579、0.601 和 0.633（<0.05）。然而，它们在 SCLC 和 NC 组之间均无显著差异，仅针对 JAK2 的 IgM 自身抗体在 LUSC 中的表达高于 NC（=0.046）。通过逻辑回归分析，结合五种 IgM 自身抗体和癌胚抗原（CEA），构建了一个用于 LUAD 的诊断模型。该模型的 AUC 为 0.827（敏感性=56.63%，特异性=93.98%）。该诊断效率优于 CEA（AUC=0.692）或 IgM 自身抗体单独（AUC=0.698）。值得注意的是，该模型在早期 LUAD 中的准确性达到 83.02%。总之，我们发现并鉴定了五种新型 IgM 指标，并开发了一种结合 IgM 自身抗体和 CEA 的多分析物模型，这可能是一种有价值且强大的辅助诊断工具，并可能提高 LUAD 的早期检测准确性。
