ABSTRACT Autoantibodies (AAbs) represent promising biomarkers in cancer. While most AAbs are elevated in cancer, a substantial subset is downregulated, and their diagnostic and prognostic potential remains largely unexplored. Here we used the HuProt protein microarray to identify downregulated AAbs in non‐small cell lung cancer (NSCLC) serum. Indirect ELISA quantified serum levels in 781 samples. Ten machine learning algorithms were used to construct diagnostic models. An independent cohort of 353 NSCLC patients was used to assess prognostic value and develop a prognostic model. Six downregulated AAbs were identified, among which five AAbs (anti‐HIST1H1B, anti‐HIST1H1C, anti‐DYDC2, anti‐CAMKK2, and anti‐GRPEL1) were significantly reduced in NSCLC. The gradient boosting machine (GBM) model showed the best performance for NSCLC and BPNs, with AUCs of 0.869 (95% CI: 0.833–0.905) in the training set and 0.813 (95% CI: 0.745–0.880) in the validation set. For early‐stage NSCLC, the model achieved an AUC of 0.809 (95% CI: 0.729–0.890) in the validation set, with a sensitivity of 74.0% and specificity of 81.3%. Multivariate Cox regression identified four AAbs significantly associated with patient prognosis. A prognostic model integrating age and AAb levels demonstrated robust predictive performance for long‐term survival (7‐year AUC = 0.79). Bioinformatics analyses further supported the relevance of the corresponding genes/proteins of these AAbs to NSCLC outcomes. Overall, our findings demonstrate that downregulated AAbs possess significant diagnostic and prognostic value in NSCLC and may contribute to improved patient management and survival prediction.
Liang et al. (Tue,) studied this question.