Integrating thermal liquid biopsy, clinical data, and mass spectrometry for early diagnosis and biomarker discovery in colorectal cancer

Early detection of colorectal cancer is essential to improving survival, where yet current diagnostic tools show limited performance. This study aimed to enhance diagnostic accuracy by integrating clinical variables with thermogram profiles obtained through serum-based thermal liquid biopsy and analyzed using machine learning models. We evaluated 328 patients with colorectal cancer and 355 symptomatic individuals with non-organ-specific cancer signs but negative diagnostic evaluations, to reproduce clinically relevant decision settings. The combined model showed improved classification performance compared with the use of clinical variables alone, particularly in patients with early-stage disease. In addition, proteomic analysis of samples stratified by thermogram patterns identified proteins associated with survival, including fibrinogen-like protein 1, supporting the biological relevance of these thermodynamic profiles. Together, these findings indicate that integrating serum thermogram information with routine clinical data can modestly strengthen diagnostic assessment and help identify biologically meaningful patient subgroups, offering a promising non-invasive colorectal cancer evaluation.