Abstract
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.
Highlights
With estimates of 2.2 million new cases and 1.8 million deaths, lung cancer (LC) is the second most common cancer and the leading cause of cancer death worldwide, approximately accounting for one-tenth (11.4%) of cancer occurrence and one-fifth (18.0%) of cancer deaths [1]
147 lung adenocarcinoma (LUAD), 147 matched normal controls (NCs), 72 lung squamous cell carcinoma (LUSC) patients, and 44 small cell lung carcinoma (SCLC) patients were included in the validation cohort
In phase I, the serum samples from the discovery cohort composed of 68 LUADs and 68 matched NCs were individually profiled on the protein array for the screening of Immunoglobulin M (IgM) autoantibodies
Summary
With estimates of 2.2 million new cases and 1.8 million deaths, lung cancer (LC) is the second most common cancer and the leading cause of cancer death worldwide, approximately accounting for one-tenth (11.4%) of cancer occurrence and one-fifth (18.0%) of cancer deaths [1]. The 5-year survival rate is 57% for patients with localized tumors, while this decreased to 5% for patients at the metastatic stage [2], which indicates that the high mortality rate of LC is closely related to cancer stage. LC is classified into non-small cell lung carcinoma (NSCLC) and small cell lung carcinoma (SCLC). There are two major types of NSCLC: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) [3]. LUAD, as the most common lung malignancy, is frequently found in women and non-smokers [3, 4]. A large number of studies have shown that, if LC patients with adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) could undergo radical surgery, their 5-year disease-free survival rate may approach 100% [5]. The early diagnosis and treatment of LUAD are essential to reduce the mortality of LC
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