Differential Expression Analysis of Cutaneous Squamous Cell Carcinoma and Basal Cell Carcinoma Proteomic Profiles Sampled with Electroporation-Based Biopsy

Edward Vitkin, Julia Wise, Ariel Berl, Ofir Shir-az, Batel Gabay, Amrita Singh, Vladimir Kravtsov, Zohar Yakhini, Avshalom Shalom, Alexander Golberg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) affects treatment plans. We report a tissue sampling approach with molecular biopsy using electroporation. This method, coined electroporation-based biopsy (e-biopsy), enables nondestructive nonthermal permeabilization of cells in the skin for vacuum-assisted extraction of biomolecules. We used e-biopsy for ex vivo proteome extraction from 3 locations per patient in 21 cSCC, 20 BCC, and 7 actinic keratosis human skin samples. Using liquid chromatography with tandem mass spectrometry, we identified 5966 proteins observed with nonzero intensity in at least 1 sample. The intrapatient Pearson correlation of 0.888 ± 0.065 for patients with BCC, 0.858 ± 0.077 for patients with cSCC, and 0.876 ± 0.116 for those with solar actinic keratosis indicates high consistency of the e-biopsy sampling. The mass spectra presented significantly different proteome profiles for cSCC, BCC, and solar actinic keratosis, with several hundreds of proteins differentially expressed. Notably, our study showed that proteomes sampled with e-biopsy from cSCC and BCC lesions are different and that proteins of CRNN, SULT1E1, and ITPK1 genes are significantly overexpressed in BCC in comparison with those in cSCC. Our results provide evidence that the e-biopsy approach could potentially be used as a tool to support cutaneous lesions classification with molecular pathology.

Original languageEnglish
Article number100304
JournalJID Innovations
Volume4
Issue number6
DOIs
StatePublished - Nov 2024

Funding

FundersFunder number
TAU Zimin Center
Israel Ministry of Science and Technology, Israel Innovation
Technion-Israel Institute of Technology
Smoler Proteomics Center

    Keywords

    • Diagnostics
    • Finite elements modeling
    • Machine learning
    • Molecular pathology
    • Protein–protein interaction
    • Skin cancer

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