INTRODUCTION: Tumor-associated antigens (TAA) are proteins expressed during the growth and development of tumor cells, and TAA autoantibodies (TAAbs) can be detected in the serum of lung cancer patients, which can be utilized in the early screening of lung cancer. Almost all the TAAbs applied for diagnosis are those elevated, however, there are still large numbers of autoantibodies detected to decrease in tumor serums, and their functions were rarely known. Diagnosing malignant small lung nodules (≤3cm) in CT scans remains a challenge in clinical practice. METHODS: In this study, we applied the HuProt array and the bioinformatics analysis to assess the diagnostic values of the decreased autoantibodies in lung cancers. RESULTS: In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. We combined with 4 elevated TAAbs, the sensitivity and the specificity of the 10-marker model can attain 80.0% and 87.0%, respectively, which is higher than that of the commonly used 7-TAAbs model in diagnosis for early-stage lung cancer. Moreover, 5 of the decreased autoantibodies can also be applied for supervising bone metastasis in lung adenocarcinoma. A follow-up process for 13 patients diagnosed with early-stage lung cancer revealed that 10 of the 15 decreased autoantibodies would recover to a higher level after the tumor was resected. Bioinformatic analysis indicated that the 15 biomarkers were strongly correlated with the prognosis of lung cancer patients. CONCLUSION: We confirmed the importance of the decreased autoantibodies in lung cancer, providing new diagnostic and therapeutic strategies.