Background: Keratinocytic skin lesions (KSLs), including pre-malignant actinic keratosis (AK) and Bowen's disease (BD), and invasive cutaneous squamous cell carcinoma (cSCC) can be challenging to diagnose using clinical criteria. Histopathological assessment of an invasive biopsy can also be inaccurate and uncomfortable for patients, making a non-invasive diagnostic approach necessary.
Methods: This study used non-invasive sampling by tape-stripping coupled with data-independent acquisition mass spectrometry (DIA-MS) proteomics to profile the proteome of 61 histopathologically diagnosed AK, BD, and cSCC, as well as matched normal stratum corneum samples collected from 26 patients. Proteomic data were analysed to identify proteins and biological functions that are significantly different between KSLs. Additionally, a support vector machine (SVM) machine learning algorithm was used to assess the usefulness of proteomic data for KSL classification.
Results: A total of 696 proteins were identified across the samples studied. SVM classification analysis of the proteomic data correctly identified 73.34% of premalignant lesions (AK+BD) and 81.25% of cSCCs. 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-value <0.05). Bioinformatic analysis of the data predicted changes in pivotal carcinogenic pathways such as LXR/RXR activation, production of reactive oxygen species, and Hippo signalling that may explain the progression of cSCC from premalignant lesions.
Conclusion: This study demonstrates that DIA-MS analysis of tape-stripped samples can identify non-invasive protein biomarkers in KSLs with the potential to be developed into a complementary diagnostic tool.