Abstract

Early cutaneous squamous cell carcinoma (cSCC) can be challenging to diagnose using clinical criteria as it could present similar to actinic keratosis (AK) or Bowen's disease (BD), precursors of cSCC. Currently, histopathological assessment of an invasive biopsy is the gold standard for diagnosis. A non-invasive diagnostic approach would reduce patient and health system burden. Therefore, this study used non-invasive sampling by tape-stripping coupled with data-independent acquisition mass spectrometry (DIA-MS) proteomics to profile the proteome of histopathologically diagnosed AK, BD and cSCC, as well as matched normal samples. Proteomic data were analysed to identify proteins and biological functions that are significantly different between lesions. Additionally, a support vector machine (SVM) machine learning algorithm was used to assess the usefulness of proteomic data for the early diagnosis of cSCC. A total of 696 proteins were identified across the samples studied. A machine learning model constructed using the proteomic data classified premalignant (AK + BD) and malignant (cSCC) lesions at 77.5% accuracy. Differential abundance analysis identified 144 and 21 protein groups that were significantly changed in the cSCC, and BD samples compared to the normal skin, respectively (adj. p < 0.05). Changes in pivotal carcinogenic pathways such as LXR/RXR activation, production of reactive oxygen species, and Hippo signalling were observed that may explain the progression of cSCC from premalignant lesions. In summary, this study demonstrates that DIA-MS analysis of tape-stripped samples can identify non-invasive protein biomarkers with the potential to be developed into a complementary diagnostic tool for early cSCC.