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 (p < 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 (p = 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.