The squamous cell carcinoma antigen (SCCA) is a tumor marker that has gained increasing attention for its biological functions and significance in normal physiological and pathological processes. Not only SCCA but also circulating immune complexes of SCCA and immunoglobulin M (IgM) are involved in normal physiological and pathological processes, providing a background for numerous clinical studies aimed at assessing the potential role of SCCA, SCCA–IgM, and SCCA isoform complexes in clinical practice. Previous studies support the clinical value of SCCA as a tumor marker for either diagnosing squamous cancers or monitoring the response to radiotherapy or chemotherapy, tumor relapse, and treatment failure. However, these studies show contrasting results, making the diagnostic or prognostic value of SCCA controversial. To reduce clinical heterogeneity across studies and achieve a more accurate and reliable comparison of results, a standardized detection method, scoring system, and cutoff level need to be established. Moreover, despite the fact that performances of different methods are comparable, the dynamic observation of tumor marker kinetics should be conducted under the same method.

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.