An integrated serum peptidomics and multi-algorithm machine learning approach for early diagnosis of lung cancer

Background Lung cancer is one of the leading causes of cancer-related mortality worldwide. Due to the lack of specific symptoms at early stages, most patients are diagnosed at advanced stages, underscoring the need for reliable non-invasive early detection strategies beyond invasive biopsy and non-specific imaging. Methods We integrated serum proteomics with multi-algorithm machine learning to establish an artificial intelligence–based workflow for early lung cancer diagnosis. Serum samples from 774 participants were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to generate peptidomic profiles. After data preprocessing and normalization, eight machine learning algorithms were applied for model construction, and model performance was systematically evaluated using the receiver operating characteristic (ROC) curve. Results Most machine learning models showed strong diagnostic performance in independent test sets. By combining feature importance, Shapley additive explanations (SHAP), and local interpretable model-agnostic explanations (LIME), nine lung cancer–associated peptide peaks were identified. Multidimensional models based on these peptide signatures achieved AUC values approaching 1, significantly outperforming single markers derived from the PRIDE database. Candidate proteins were further validated through integrative transcriptomic and proteomic analyses, supporting their biological relevance. Conclusions Our findings indicate that serum proteomics combined with multi-algorithm machine learning enables accurate and non-invasive early detection of lung cancer, providing a promising framework for clinical translation.